Gait Recognition-Based Human Identification and Gender Classification

The main objective of this work is to identify the persons and to classify the gender of those persons with the help of their walking styles from the gait sequences with arbitrary walking directions. The human silhouettes are extracted from the given gait sequences using background subtraction technique. Median value approach is used for the background subtraction. After the extraction of the silhouettes, the affinity propagation clustering is performed to group the silhouettes with similar views and poses to one cluster. The cluster-based averaged gait image is taken as a feature for each cluster. To learn the distance metric, sparse reconstruction-based metric learning has been used. It minimizes the intraclass sparse reconstruction errors and maximizes the interclass reconstruction errors simultaneously. The above-mentioned steps have come under the training phase. With the help of the metric learned in the training and the feature extracted from the testing video sequence, sparse reconstruction-based classification has been performed for identifying the person and gender classification of that person. The accuracy achieved for the human identification and gender classification is promising.