Face sketch-photo recognition using local gradient checksum: LGCS

Face sketch-photo recognition using local gradient checksum: LGCS A new approach for matching of face sketch images with face photo images and vice versa has been presented here. For the extraction of local edge features from both the sketch and photo images, a new local feature called local gradient checksum (LGCS) has been developed. LGCS is a modality reduction local edge feature on gradient domain. It is calculated as the summation of four pairs of gradient differences between two local pixels that are at 180° with each other. The Euclidean distance between query sketch and gallery of photos are measured depending on extracted LGCS features. To further improve the result, a multi-scale LGCS is proposed. A rank-1 accuracy of 100 % is achieved in a gallery of 606 photos consisting of CUHK, AR, and XM2VTS face dataset. The proposed face sketch-photo recognition system requires neither learning procedures nor training data. Further, the experiment is extended to test the robustness of the proposed algorithm on blurred, noisy and disguised sketches, as well as photos. Under those situations also, LGCS has outperformed center-symmetric local binary pattern, directional local extrema pattern and weber local descriptor feature extraction techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

Face sketch-photo recognition using local gradient checksum: LGCS

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Springer Berlin Heidelberg
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
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