Sadaf Asif

Work place: University Of Engineering and Technology, Taxila Pakistan



Research Interests: Image Processing, Image Compression, Computer Vision, Software Development Process, Software Construction, Software


Engr. Sadaf Asif received her BSc degree from University of Engineering and Technology, Taxila, Pakistan in Software Engineering. She is currently working as research associate in University Of Engineering and Technology Taxila. Her current research interests include Computer Vision, Image Processing, Satellite Communication, and Software Development.

Author Articles
Human Identification On the basis of Gaits Using Time Efficient Feature Extraction and Temporal Median Background Subtraction

By Sadaf Asif Engr Ali Javed Muhammad Irfan

DOI:, Pub. Date: 8 Feb. 2014

Gait analysis is basically referred to study of human locomotion. From the surveillance point of view behavioral biometrics and recognition at a distance are becoming more popular in researchers rather than interactive and Physiological biometrics. In this paper, a time efficient Human gait identification system is proposed. Initially Human silhouettes are extracted by using temporal median background subtraction on video frames, which successfully removes shadows and models even complex background, proposed gait algorithm extracts contours from foreground silhouettes images and then three bounding boxes are drawn around contoured human image 1) upper part for arms movement 2) middle part for thigh and knee angles 3) Lower part for legs movement, knee and ankle angles. Gait cycles are extracted to find gait period and to take final decision for gait features selection, which is used for training. Thigh, Knee, Ankle angles and bounding boxes' widths are used as gait signatures but middle portion of human contains less variations of width in gait cycle hence computing efficiency can be achieved by ignoring width factor of middle part. SVM based training and identification is performed on extracted gait features. The proposed system is assessed using publicly available gait datasets and some indoor experimental videos created for this research work. The results reveal that the proposed algorithm is able to achieve an outstanding recognition rate.

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