Conference Papers
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Item Frontal Gait Recognition based on Hierarchical Centroid Shape Descriptor and Similarity Measurement(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.Gait recognition is an expanding stream in biometrics, intended to recognize individuals through the investigation of their walking pattern. This pattern is obtained from a distance, without the active participation of the people. One of the difficulties of the appearance-based gait approach is to enhance the performance of frontal gait recognition, as it carries less spatial and temporal data when compared with other view variations. As a result, to increase the performance of the frontal gait recognition, this paper presents a method which uses two-step procedure; the Hierarchical centroid Shape descriptor (HCSD) and the similarity measurement. The proposed method was assessed on the broadly used CASIA A, CASIA B, and CMU MoBo gait databases. The experimental outcomes showed that the proposed method gave promising results and outperforms certain state-of-the-art methods in terms of recognition performance. © 2019 IEEE.Item An Approach to Speed Invariant Gait Analysis for Human Recognition using Mutual Information(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.Gait is a biometric characteristic that facilitates the identification of individuals with low-resolution images. This aspect intensifies its utility in many human detection applications. However, there are many challenges that adversely affect the gait recognition performance. They are caused by the impact of various covariate aspects such as, changes in clothing and carrying conditions, walking speed, walking surface conditions, view variations, and so on. This paper proposes an effective approach for speed-invariant gait recognition system. This approach uses the Region of Interest (ROI) extracted from Gait Energy Image (GEI) to classify a probe sample into a gallery sample. The mutual information obtained from a probe and gallery sample, followed by their classification capture the spatial dynamics of GEI efficiently to improve the gait recognition performance. Further, the proposed method is evaluated on CASIA C and OU-ISIR Treadmill A gait databases. Experimental results demonstrate the capability of the proposed approach in comparison with the existing gait recognition methods. © 2019 IEEE.Item Gaussian Filtered Gait Energy Template and Centroid Corner Distance Features for Human Gait Recognition(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.One of the convincing and latest biometric systems is gait recognition because of its ability to unobtrusively identify an individual at a distance and with low-resolution images. This study proposes an efficient method to enhance the performance of the gait detection system. The gait silhouette images are initially processed with two gait portrayal methods as the feature resources: Gait Energy Image (GEI) and Gaussian Filtered-Gait Energy Image (GF-GEI). Further, an effort has been made to present a statistical shape examination method, which is established on GF-GEI, and it is divided into six independent horizontal segments. The centroid corner distance features obtained from these horizontal segments forms the feature vector of the image. The proposed method is assessed on the widely used CASIA A, CASIA B, and OU-ISIR D gait datasets. The empirical results illustrate that the performance of the proposed approach is promising and surpasses some state-of-the-art gait identification methods recorded in literature. © 2019 IEEE.Item Improved Robustness of EMG Pattern Recognition for Transradial Amputees with EMG Features Against Force Level Variations(Institute of Electrical and Electronics Engineers Inc., 2023) Powar, O.S.; Chemmangat, K.Feature extraction is an essential process for removing the unwanted part and interference of the Electromyography (EMG) signal, and to extract the useful information hidden in it. Inorder to obtain high performance of Myoelectric Control (MEC), the choice of features plays an important role. The studies carried out earlier to overcome force level variation have used features which are redundant, affecting the robustness and the classification performance. This study's main objective is to assess a database's performance consisting of nine upper limb amputee subjects with EMG data recorded at three different force levels when six motions were classified using twenty different time domain features that are frequently found in the literature. Training is carried out at one force level, and the other two unknown force levels are used for testing. Out of the twenty features, the one that is the most stable is displayed for each force level. The results show that root mean square (RMS) feature outperformed other features for training at low and medium force levels, and Wilson amplitude (WAMP) feature for training at a high force level, when compared with the most widely used linear discriminant analysis (LDA) classifier. The average classification accuracy for the nine amputee subjects trained with the RMS feature at low and medium force levels was 42% and 51.78% percent, respectively. For high force level, when trained using WAMP feature, an accuracy of 46.78% has been obtained. The features are verified using histogram plots. This study will help select those features which are not important for robust classification of hand movements. © 2023 IEEE.Item Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities(Institute of Electrical and Electronics Engineers Inc., 2024) Anusha, R.; Sanshi, S.Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.