FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences

Security threats are always there if the human intruders are not identified and recognized well in time in highly security-sensitive environments like the military, airports, parliament houses, and banks. Fog computing and machine learning algorithms on Gait sequences can prove to be better for restricting intruders promptly. Gait recognition provides the ability to observe an individual unobtrusively, without any direct cooperation or interaction from the people, making it very attractive than other biometric recognition techniques. In this paper, a Fog Computing and Machine Learning Inspired Human Identity and Gender Recognition using Gait Sequences (FCML-Gait) are proposed. Internet of things (IoT) devices and video capturing sensors are used to acquire data. Frames are clustered using the affinity propagation (AP) clustering technique into several clusters, and cluster-based averaged gait image(C-AGI) feature is determined for each cluster. For training and testing of datasets, sparse reconstruction-based metric learning (SRML) and Speeded Up Robust Features (SURF) with support vector machine (SVM) are applied on benchmark gait database ADSC-AWD having 80 subjects of 20 different individuals in the Fog Layer to improve the processing. The performance metrics, for instance, accuracy, precision, recall, F-measure, C-time, and R-time have been measured, and a comparative evaluation of the projected method with the existing SRML technique has been provided in which the proposed FCML-Gait outperforms and attains the highest accuracy of 95.49%.

Deep supervised hashing for gait retrieval

Background: Gait recognition is perceived as the most promising biometric approach for future decades especially because of its efficient applicability in surveillance systems. Due to recent growth in the use of gait biometrics across surveillance systems, the ability to rapidly search for the required data has become an emerging need. Therefore, we addressed the gait retrieval problem, which retrieves people with gaits similar to a query subject from a large-scale dataset. Methods: This paper presents the deep gait retrieval hashing (DGRH) model to address the gait retrieval problem for large-scale datasets. Our proposed method is based on a supervised hashing method with a deep convolutional network. We use the ability of the convolutional neural network (CNN) to capture the semantic gait features for feature representation and learn the compact hash codes with the compatible hash function. Therefore, our DGRH model combines gait feature learning with binary hash codes. In addition, the learning loss is designed with a classification loss function that learns to preserve similarity and a quantization loss function that controls the quality of the hash codes Results: The proposed method was evaluated against the CASIA-B, OUISIR-LP, and OUISIR-MVLP benchmark datasets and received the promising result for gait retrieval tasks. Conclusions: The end-to-end deep supervised hashing model is able to learn discriminative gait features and is efficient in terms of the storage memory and speed for gait retrieval.

Deep supervised hashing for gait retrieval

Background: Gait recognition is perceived as the most promising biometric approach for future decades especially because of its efficient applicability in surveillance systems. Due to recent growth in the use of gait biometrics across surveillance systems, the ability to rapidly search for the required data has become an emerging need. Therefore, we addressed the gait retrieval problem, which retrieves people with gaits similar to a query subject from a large-scale dataset. Methods: This paper presents the deep gait retrieval hashing (DGRH) model to address the gait retrieval problem for large-scale datasets. Our proposed method is based on a supervised hashing method with a deep convolutional network. We use the ability of the convolutional neural network (CNN) to capture the semantic gait features for feature representation and learn the compact hash codes with the compatible hash function. Therefore, our DGRH model combines gait feature learning with binary hash codes. In addition, the learning loss is designed with a classification loss function that learns to preserve similarity and a quantization loss function that controls the quality of the hash codes Results: The proposed method was evaluated against the CASIA-B, OUISIR-LP, and OUISIR-MVLP benchmark datasets and received the promising result for gait retrieval tasks. Conclusions: The end-to-end deep supervised hashing model is able to learn discriminative gait features and is efficient in terms of the storage memory and speed for gait retrieval.