We present a prototype video tracking and person categorization system that uses face and person soft biometric features
to tag people while tracking them in multiple camera views. Our approach takes advantage of temporal aspect of video
by extracting and accumulating feasible soft biometric features for each person in every frame to build a dynamic soft
biometric feature list for each tracked person in surveillance videos. We developed algorithms for extracting face soft
biometric features to achieve gender and ethnicity classification and session soft biometric features to aid in camera
hand-off in surveillance videos with low resolution and uncontrolled illumination. To train and test our face soft
biometry algorithms, we collected over 1500 face images from both genders and three ethnicity groups with various
sizes, poses and illumination. These soft biometric feature extractors and classifiers are implemented on our existing
video content extraction platform to enhance video surveillance tasks. Our algorithms achieved promising results for
gender and ethnicity classification, and tracked person re-identification for camera hand-off on low to good quality
surveillance and broadcast videos. By utilizing the proposed system, a high level description of extracted person's soft
biometric data can be stored to use later for different purposes, such as to provide categorical information of people, to
create database partitions to accelerate searches in responding to user queries, and to track people between cameras.
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