Abstract
Facial makeup is a global problem from the perspective of recognition and security. In this paper, a hybrid feature extraction method is proposed for makeup-invariant face identification and verification. The Gabor Filter Bank (GFB) and Histogram of Oriented Gradients (HOG) were applied to face images from the Virtual Makeup (VMU) database for feature extraction. The final feature vectors were generated through the combination of GFB and HOG features and classified using the City Block Distance (CBD), Euclidean Distance (EUC) and Cosine Similarity Metric (CSM). Performance evaluation of the CBD, EUC and CSM classifiers produced identification and verification rates of 97.39% and 94.12%, 96.73% and 92.16%, and 94.77% and 89.54% respectively for the VMU database. The CSM has the least recognition rate while the CBD achieved the best recognition rates. The implemented method outclassed several face recognition methods previously developed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
