Uncorrelated discriminant simplex analysis for view-invariant gait signal computing

Coupled Patch Alignment for Matching Cross-view Gaits

Gait recognition has attracted growing attention in recent years as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a Coupled Patch Alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest-neighbors. Then we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with Canonical Correlation Analysis (CCA). Algorithmically, we extend CPA to "Multi-dimensional Patch Alignment" (MPA) that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.

Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers

Biometrics is the technique of automatically recognizing individuals based on their biological or behavioral characteristics. Various biometric traits have been introduced and widely investigated, including fingerprint, iris, face, voice, palmprint, gait and so forth. Apart from identity, biometric data may convey various other personal information, covering affect, age, gender, race, accent, handedness, height, weight, etc. Among these, analysis of demographics (age, gender, and race) has received tremendous attention owing to its wide real-world applications, with significant efforts devoted and great progress achieved. This survey first presents biometric demographic analysis from the standpoint of human perception, then provides a comprehensive overview of state-of-the-art advances in automated estimation from both academia and industry. Despite these advances, a number of challenging issues continue to inhibit its full potential. We second discuss these open problems, and finally provide an outlook into the future of this very active field of research by sharing some promising opportunities.

View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition

Cross-view gait recognition authenticates a person using a pair of gait image sequences with different observation views. View difference causes degradation of gait recognition accuracy, and so several solutions have been proposed to suppress this degradation. One useful solution is to apply a view transformation model (VTM) that encodes a joint subspace of multiview gait features trained with auxiliary data from multiple training subjects, who are different from test subjects (recognition targets). In the VTM framework, a gait feature with a destination view is generated from that with a source view by estimating a vector on the trained joint subspace, and gait features with the same destination view are compared for recognition. Although this framework improves recognition accuracy as a whole, the fit of the VTM depends on a given gait feature pair, and causes an inhomogeneously biased dissimilarity score. Because it is well known that normalization of such inhomogeneously biased scores improves recognition accuracy in general, we therefore propose a VTM incorporating a score normalization framework with quality measures that encode the degree of the bias. From a pair of gait features, we calculate two quality measures, and use them to calculate the posterior probability that both gait features originate from the same subjects together with the biased dissimilarity score. The proposed method was evaluated against two gait datasets, a large population gait dataset of over-ground walking (course dataset) and a treadmill gait dataset. The experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.

Gait-based person recognition using arbitrary view transformation model

Gait recognition is a useful biometric trait for person authentication because it is usable even with low image resolution. One challenge is robustness to a view change (cross-view matching); view transformation models (VTMs) have been proposed to solve this. The VTMs work well if the target views are the same as their discrete training views. However, the gait traits are observed from an arbitrary view in a real situation. Thus, the target views may not coincide with discrete training views, resulting in recognition accuracy degradation. We propose an arbitrary VTM (AVTM) that accurately matches a pair of gait traits from an arbitrary view. To realize an AVTM, we first construct 3D gait volume sequences of training subjects, disjoint from the test subjects in the target scene. We then generate 2D gait silhouette sequences of the training subjects by projecting the 3D gait volume sequences onto the same views as the target views, and train the AVTM with gait features extracted from the 2D sequences. In addition, we extend our AVTM by incorporating a part-dependent view selection scheme (AVTM_PdVS), which divides the gait feature into several parts, and sets part-dependent destination views for transformation. Because appropriate destination views may differ for different body parts, the part-dependent destination view selection can suppress transformation errors, leading to increased recognition accuracy. Experiments using data sets collected in different settings show that the AVTM improves the accuracy of cross-view matching and that the AVTM_PdVS further improves the accuracy in many cases, in particular, verification scenarios.

UNCORRELATED LOCAL FISHER DISCRIMINANT ANALYSIS FOR FACE RECOGNITION

An improved manifold learning method, called Uncorrelated Local Fisher Discriminant Analysis (ULFDA), for face recognition is proposed. Motivated by the fact that statistically uncorrelated features are desirable for dimension reduction, we propose a new difference-based optimization objective function to seek a feature submanifold such that the within-manifold scatter is minimized, and between-manifold scatter is maximized simultaneously in the embedding space. We impose an appropriate constraint to make the extracted features statistically uncorrelated. The uncorrelated discriminant method has an analytic global optimal solution, and it can be computed based on eigen decomposition. As a result, the proposed algorithm not only derives the optimal and lossless discriminative information, but also guarantees that all extracted features are statistically uncorrelated. Experiments on synthetic data and AT&T, extended YaleB and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of the proposed method.