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Face recognition across makeup and plastic surgery from real-world images

[+] Author Affiliations
Ali Moeini, Karim Faez

Amirkabir University of Technology, Department of Electrical Engineering, Hafez Avenue, Tehran 15914, Iran

Hossein Moeini

Semnan University, Department of Electrical Engineering, Daneshgah Avenue, Semnan 35131, Iran

J. Electron. Imaging. 24(5), 053028 (Oct 20, 2015). doi:10.1117/1.JEI.24.5.053028
History: Received May 31, 2015; Accepted September 18, 2015
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Abstract.  A study for feature extraction is proposed to handle the problem of facial appearance changes including facial makeup and plastic surgery in face recognition. To extend a face recognition method robust to facial appearance changes, features are individually extracted from facial depth on which facial makeup and plastic surgery have no effect. Then facial depth features are added to facial texture features to perform feature extraction. Accordingly, a three-dimensional (3-D) face is reconstructed from only a single two-dimensional (2-D) frontal image in real-world scenarios. Then the facial depth is extracted from the reconstructed model. Afterward, the dual-tree complex wavelet transform (DT-CWT) is applied to both texture and reconstructed depth images to extract the feature vectors. Finally, the final feature vectors are generated by combining 2-D and 3-D feature vectors, and are then classified by adopting the support vector machine. Promising results have been achieved for makeup-invariant face recognition on two available image databases including YouTube makeup and virtual makeup, and plastic surgery-invariant face recognition on a plastic surgery face database is compared to several state-of-the-art feature extraction methods. Several real-world scenarios are also planned to evaluate the performance of the proposed method on a combination of these three databases with 1102 subjects.

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Citation

Ali Moeini ; Karim Faez and Hossein Moeini
"Face recognition across makeup and plastic surgery from real-world images", J. Electron. Imaging. 24(5), 053028 (Oct 20, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053028


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