201
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Tafazzoli F, Bebis G, Louis S, Hussain M. Improving Human Gait Recognition Using Feature Selection. ADVANCES IN VISUAL COMPUTING 2014. [DOI: 10.1007/978-3-319-14364-4_80] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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202
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203
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204
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Guan Y, Wei X, Li CT. On the Generalization Power of Face and Gait in Gender Recognition. INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS 2014. [DOI: 10.4018/ijdcf.2014010101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.
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Affiliation(s)
- Yu Guan
- Department of Computer Science, University of Warwick, Warwick, Coventry, UK
| | - Xingjie Wei
- Department of Computer Science, University of Warwick, Warwick, Coventry, UK
| | - Chang-Tsun Li
- Department of Computer Science, University of Warwick, Warwick, Coventry, UK
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205
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HU MAODI, WANG YUNHONG, ZHANG ZHAOXIANG. CROSS-VIEW GAIT RECOGNITION WITH SHORT PROBE SEQUENCES: FROM VIEW TRANSFORMATION MODEL TO VIEW-INDEPENDENT STANCE-INDEPENDENT IDENTITY VECTOR. INT J PATTERN RECOGN 2013. [DOI: 10.1142/s0218001413500171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Considering it is difficult to guarantee that at least one continuous complete gait cycle is captured in real applications, we address the multi-view gait recognition problem with short probe sequences. With unified multi-view population hidden markov models (umvpHMMs), the gait pattern is represented as fixed-length multi-view stances. By incorporating the multi-stance dynamics, the well-known view transformation model (VTM) is extended into a multi-linear projection model in a four-order tensor space, so that a view-independent stance-independent identity vector (VSIV) can be extracted. The main advantage is that the proposed VSIV is stable for each subject regardless of the camera location or the sequence length. Experiments show that our algorithm achieves encouraging performance for cross-view gait recognition even with short probe sequences.
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Affiliation(s)
- MAODI HU
- School of Computer Science and Engineering, Beihang University, Beijing 100191, P. R. China
- Digital Technology Academy, Aisino Corporation, Beijing 100195, P. R. China
| | - YUNHONG WANG
- School of Computer Science and Engineering, Beihang University, Beijing 100191, P. R. China
| | - ZHAOXIANG ZHANG
- School of Computer Science and Engineering, Beihang University, Beijing 100191, P. R. China
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206
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Boulgouris NV, Huang X. Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:3636-3647. [PMID: 23751961 DOI: 10.1109/tip.2013.2266578] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods.
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Affiliation(s)
- Nikolaos V Boulgouris
- Department of Electronic and Computer Engineering, Brunel University, London UB8 3PH, UK.
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207
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Gait-based person identification robust to changes in appearance. SENSORS 2013; 13:7884-901. [PMID: 23783739 PMCID: PMC3715270 DOI: 10.3390/s130607884] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2013] [Revised: 06/10/2013] [Accepted: 06/14/2013] [Indexed: 11/17/2022]
Abstract
The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects' appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance. In this paper, we propose a novel method, which allows robustly identifying people in spite of changes in appearance, without using a database of predicted appearance changes. In the proposed method, firstly, the human body image is divided into multiple areas, and features for each area are extracted. Next, a matching weight for each area is estimated based on the similarity between the extracted features and those in the database for standard clothes. Finally, the subject is identified by weighted integration of similarities in all areas. Experiments using the gait database CASIA show the best correct classification rate compared with conventional methods experiments.
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208
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209
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Haines TSF, Xiang T. Active Rare Class Discovery and Classification Using Dirichlet Processes. Int J Comput Vis 2013. [DOI: 10.1007/s11263-013-0630-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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210
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Lee CP, Tan AW, Tan SC. Gait recognition via optimally interpolated deformable contours. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2013.01.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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211
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A Complexity Measure of Gait Perception. PATTERN RECOGNITION AND IMAGE ANALYSIS 2013. [DOI: 10.1007/978-3-642-38628-2_58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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212
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213
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Kusakunniran W, Qiang Wu, Jian Zhang, Hongdong Li. Gait Recognition Across Various Walking Speeds Using Higher Order Shape Configuration Based on a Differential Composition Model. ACTA ACUST UNITED AC 2012; 42:1654-68. [DOI: 10.1109/tsmcb.2012.2197823] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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214
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Wang C, Zhang J, Wang L, Pu J, Yuan X. Human identification using temporal information preserving gait template. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2012; 34:2164-2176. [PMID: 22201053 DOI: 10.1109/tpami.2011.260] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.
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Affiliation(s)
- Chen Wang
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China.
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215
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Ben X, Meng W, Yan R, Wang K. An improved biometrics technique based on metric learning approach. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.06.022] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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216
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Zeng W, Wang C. Human gait recognition via deterministic learning. Neural Netw 2012; 35:92-102. [DOI: 10.1016/j.neunet.2012.07.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2012] [Revised: 06/24/2012] [Accepted: 07/30/2012] [Indexed: 11/28/2022]
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217
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Roy A, Sural S, Mukherjee J. A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification. Pattern Recognit Lett 2012. [DOI: 10.1016/j.patrec.2012.02.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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218
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Songfan Yang, Bhanu B. Understanding Discrete Facial Expressions in Video Using an Emotion Avatar Image. ACTA ACUST UNITED AC 2012; 42:980-92. [DOI: 10.1109/tsmcb.2012.2192269] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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219
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Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron. Pattern Recognit Lett 2012. [DOI: 10.1016/j.patrec.2011.04.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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220
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Huang X, Boulgouris NV. Gait recognition with shifted energy image and structural feature extraction. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:2256-2268. [PMID: 22194243 DOI: 10.1109/tip.2011.2180914] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems.
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221
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Rougier C, Auvinet E, Meunier J, Mignotte M, de Guise JA. Depth Energy Image for gait symmetry quantification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:5136-9. [PMID: 22255495 DOI: 10.1109/iembs.2011.6091272] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper introduces a new quantification method for gait symmetry based on depth information acquired from a structured light system. First, the new concept of Depth Energy Image is introduced to better visualize gait asymmetry problems. Then a simple index is computed from this map to quantify motion symmetry. Results are presented for six subjects with and without gait problems. Since the method is markerless and cheap, it could be a very promising solution in the future for gait clinics.
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Affiliation(s)
- Caroline Rougier
- Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Canada.
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222
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Xu D, Huang Y, Zeng Z, Xu X. Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:316-326. [PMID: 21724511 DOI: 10.1109/tip.2011.2160956] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l(1, 2) mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date.
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Affiliation(s)
- Dong Xu
- School of Computer Engineering, Nanyang Technological University, Singapore
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223
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Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y. The OU-ISIR Gait Database Comprising the Treadmill Dataset. ACTA ACUST UNITED AC 2012. [DOI: 10.2197/ipsjtcva.4.53] [Citation(s) in RCA: 142] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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224
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Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition. LECTURE NOTES IN COMPUTER SCIENCE 2012. [DOI: 10.1007/978-3-642-31298-4_12] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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225
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Sedmidubsky J, Valcik J, Balazia M, Zezula P. Gait Recognition Based on Normalized Walk Cycles. ADVANCES IN VISUAL COMPUTING 2012. [DOI: 10.1007/978-3-642-33191-6_2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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226
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Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos. COMPUTER VISION – ECCV 2012. WORKSHOPS AND DEMONSTRATIONS 2012. [DOI: 10.1007/978-3-642-33885-4_10] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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227
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228
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Nguyen HA, Auvinet E, Mignotte M, de Guise JA, Meunier J. Analyzing gait pathologies using a depth camera. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:4835-4838. [PMID: 23367010 DOI: 10.1109/embc.2012.6347076] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this paper, we present a new approach to construct a 3D human skeleton model, which is then used to quantify gait pathologies, using a depth camera. First, thanks to the depth map, we obtain a human depth silhouette in 3D, from which our method is based to estimate each body part position. Second, the angle between the upper and lower legs of the 3D skeleton model is calculated. Finally, we show that using only this angle information is enough to quantify motion asymmetry. This result has been verified through an experimental study with 3 different subjects. Due to its advantages (simple, markerless and low-cost), this method is a promising solution for gait clinics in the future.
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Affiliation(s)
- Hoang Anh Nguyen
- Department of Computer Science and Operations Research, University of Montreal, Montreal, QC H3C 3J7, Canada
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229
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Maodi Hu, Yunhong Wang, Zhaoxiang Zhang, De Zhang. Gait-Based Gender Classification Using Mixed Conditional Random Field. ACTA ACUST UNITED AC 2011; 41:1429-39. [DOI: 10.1109/tsmcb.2011.2149518] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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230
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231
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Makihara Y, Mannami H, Yagi Y. Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database. COMPUTER VISION – ACCV 2010 2011. [DOI: 10.1007/978-3-642-19309-5_34] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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232
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Bagher Oskuie F, Faez K, Cheraghian A, Dastmalchi H. Gait Identification Using a Novel Gait Representation: Radon Transform of Mean Gait Energy Image. PATTERN RECOGNITION AND IMAGE ANALYSIS 2011. [DOI: 10.1007/978-3-642-21257-4_48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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233
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Martín-Félez R, Mollineda RA, Sánchez JS. Human Recognition Based on Gait Poses. PATTERN RECOGNITION AND IMAGE ANALYSIS 2011. [DOI: 10.1007/978-3-642-21257-4_43] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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234
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235
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236
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Venkat I, De Wilde P. Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics. Int J Comput Vis 2010. [DOI: 10.1007/s11263-010-0362-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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237
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Affiliation(s)
- Michelle Karg
- Institute of Automatic Control Engineering, Technische Universität München, Munich, Germany.
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239
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240
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241
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Lu J, Tan YP. Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recognit Lett 2010. [DOI: 10.1016/j.patrec.2009.11.006] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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242
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Changyou Chen, Junping Zhang, Fleischer R. Distance Approximating Dimension Reduction of Riemannian Manifolds. ACTA ACUST UNITED AC 2010; 40:208-17. [DOI: 10.1109/tsmcb.2009.2025028] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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243
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Chrono-Gait Image: A Novel Temporal Template for Gait Recognition. COMPUTER VISION – ECCV 2010 2010. [DOI: 10.1007/978-3-642-15549-9_19] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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244
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Chen C, Liang J, Zhao H, Hu H, Tian J. Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit Lett 2009. [DOI: 10.1016/j.patrec.2009.04.012] [Citation(s) in RCA: 164] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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245
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Yu S, Tan T, Huang K, Jia K, Wu X. A study on gait-based gender classification. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2009; 18:1905-1910. [PMID: 19447706 DOI: 10.1109/tip.2009.2020535] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
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246
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Veeraraghavan A, Srivastava A, Roy-Chowdhury AK, Chellappa R. Rate-invariant recognition of humans and their activities. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2009; 18:1326-1339. [PMID: 19398409 DOI: 10.1109/tip.2009.2017143] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Pattern recognition in video is a challenging task because of the multitude of spatio-temporal variations that occur in different videos capturing the exact same event. While traditional pattern-theoretic approaches account for the spatial changes that occur due to lighting and pose, very little has been done to address the effect of temporal rate changes in the executions of an event. In this paper, we provide a systematic model-based approach to learn the nature of such temporal variations (time warps) while simultaneously allowing for the spatial variations in the descriptors. We illustrate our approach for the problem of action recognition and provide experimental justification for the importance of accounting for rate variations in action recognition. The model is composed of a nominal activity trajectory and a function space capturing the probability distribution of activity-specific time warping transformations. We use the square-root parameterization of time warps to derive geodesics, distance measures, and probability distributions on the space of time warping functions. We then design a Bayesian algorithm which treats the execution rate function as a nuisance variable and integrates it out using Monte Carlo sampling, to generate estimates of class posteriors. This approach allows us to learn the space of time warps for each activity while simultaneously capturing other intra- and interclass variations. Next, we discuss a special case of this approach which assumes a uniform distribution on the space of time warping functions and show how computationally efficient inference algorithms may be derived for this special case. We discuss the relative advantages and disadvantages of both approaches and show their efficacy using experiments on gait-based person identification and activity recognition.
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Affiliation(s)
- Ashok Veeraraghavan
- Centre for Automation Research and Electrical and Computer Engineering Department, University of Maryland, College Park, MD 20742, USA.
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247
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Lee M, Roan M, Smith B, Lockhart TE. Gait analysis to classify external load conditions using linear discriminant analysis. Hum Mov Sci 2009; 28:226-35. [PMID: 19162355 DOI: 10.1016/j.humov.2008.10.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2008] [Revised: 10/13/2008] [Accepted: 10/14/2008] [Indexed: 11/28/2022]
Abstract
There are many instances where it is desirable to determine, at a distance, whether a subject is carrying a hidden load. Automated detection systems based on gait analysis have been proposed to detect subjects that carry hidden loads. However, very little baseline gait kinematic analysis has been performed to determine the load carriage effect while ambulating with evenly distributed (front to back) loads on human gait. The work in this paper establishes, via high resolution motion capture trials, the baseline separability of load carriage conditions into loaded and unloaded categories using several standard lower body kinematic parameters. A total of 23 participants (19 for training and 4 for testing) were studied. Satisfactory classification of participants into the correct loading condition was achieved by employing linear discriminant analysis (LDA). Six lower body kinematic parameters including ranges of motion and path lengths from the phase portraits were used to train the LDA to discriminate loaded and unloaded walking conditions. Baseline performance from 4 participants who were not included in training data sets show that the use of LDA provides a 92.5% correct classification over two loaded and unloaded walking conditions. The results suggest that there are gait pattern changes due to external loads, and LDA could be applied successfully to classify the gait patterns with an unknown load condition.
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Affiliation(s)
- Minhyung Lee
- Vibration and Acoustics Laboratories, Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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248
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Changhong Chen, Jimin Liang, Heng Zhao, Haihong Hu, Jie Tian, Jie Tian. Factorial HMM and Parallel HMM for Gait Recognition. ACTA ACUST UNITED AC 2009. [DOI: 10.1109/tsmcc.2008.2001716] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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249
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Survey of Gait Recognition. EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS. WITH ASPECTS OF ARTIFICIAL INTELLIGENCE 2009. [DOI: 10.1007/978-3-642-04020-7_70] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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