Human gait based gender identification system using Hidden Markov Model and Support Vector Machines

Abstract

The paper presents an approach towards human gender recognition system. The Silhouettes from Center for Biometrics and Security Research (CASIA) gait database are segmented in order to identify major body points and to generate corresponding point-light display. The features such as two dimensional coordinates of major body points and joint angles are extracted from the point-light display. The features are classified using Hidden Markov Model (HMM) and Support Vector Machines (SVM). The study yields a recognition rate of 69.18% and 76.79% with 100 subject data using HMM and SVM respectively. There has been a significant improvement in recognition accuracy using joint angles as the features.

Gender classification of running subjects using full-body kinematics

“…Flora et al [7,8] perform gender classification using nonpathological gait kinematics of subjects from walking data [8] . Das et al [11] perform gender identification on gait features using a hidden markov model and SVM [11] . Gender recognition is performed in conjunction with shod/barefoot and injury classification in [26] by means of a decision stump with AdaBoost.…”

“…Current gender recognition techniques in literature have been applied to various types of data including those that are image-based [2][3][4] , audio-based [1,5,6] or gait-based [7][8][9][10][11] to name a few. Wu et al [12] categorize approaches into appearancebased approaches which include static-body, dynamic-body and apparel features in contrast to non-appearance based approaches which include data types such as speech, iris, voice and fingerprints.…”

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

“…Table 1 shows gender recognition based on the face in [7][8][9][10][11]. In [12][13][14][15], a gender is recognized based on human gait, and it can also be recognized based on footwear, color information, and human speech [16][17][18][19]. Nevertheless, these biometrics are not sufficiently robust against counterfeiting.…”

“…Method [7] • Sift features [8] • Combination of gabor filters and binary features [9] • Using hybrid of gabor filters and binary features [10] • Face_based gender recognition performance with a fuzzy inference system [11] • Using periocular biometric for gender classification in the wild [12] • From gait_based using mixed conditional random field [13] • Extraction of the hip joint data that was computed from Bio-vision hierarchical data [14] • From gait sequences with arbitrary walking directions [15] • Human gait based gender identification using Hidden Markov Model and Support Vector Machines [16] • Using footwear appearance [17] • Color information [18,19] • Speech recognition This research • Fusion of HRV and EMG signals during stepping exercise by stepper…”

“…The feature performance in each of the EMG and ECG physiological signals is sorted in decreasing order. 15 features with a bold font demonstrated in the figure is selected for further process and is denoted as the top features. While a number above the horizontal link between two modalities is denoted as the diversity between the two feature sets.…”