Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognize High Resolution Faces: From Macrocosm to Microcosm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face matching and retrieval using soft biometrics
IEEE Transactions on Information Forensics and Security
BIOFACE: a biometric face demonstrator
Proceedings of the international conference on Multimedia
A new soft biometric approach for keystroke dynamics based on gender recognition
International Journal of Information Technology and Management
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We propose to utilize micro features, namely facial marks (e.g., freckles, moles, and scars) to improve face recognition and retrieval performance. Facial marks can be used in three ways: i) to supplement the features in an existing face matcher, ii) to enable fast retrieval from a large database using facial mark based queries, and iii) to enable matching or retrieval from a partial or profile face image with marks. We use Active Appearance Model (AAM) to locate and segment primary facial features (e.g., eyes, nose, and mouth). Then, Laplacian-of-Gaussian (LoG) and morphological operators are used to detect facial marks. Experimental results based on FERET (426 images, 213 subjects) and Mugshot (1,225 images, 671 subjects) databases show that the use of facial marks improves the rank-1 identification accuracy of a state-of-the-art face recognition system from 92.96% to 93.90% and from 91.88% to 93.14%, respectively.