Abstract
The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions.
Highlights
Object recognition is a computer technology related to computer vision and image processing that deals with detecting and identifying humans, buildings, cars, etc., in digital images and video sequences
Bilel Ameur et al [14] proposed an approach where face recognition performance is significantly improved by combining Gabor wavelet and local binary pattern (LBP) for features extraction and, k-nearest neighbor (K-NN) and SRC for classification
The face image is first divided into several blocks, from which features are extracted using local binary patterns (LBP), the global feature histogram of each face is constructed
Summary
Object recognition is a computer technology related to computer vision and image processing that deals with detecting and identifying humans, buildings, cars, etc., in digital images and video sequences. It is a huge domain including face recognition which basically has two different modes: verification and identification [1]. Recognizing a person’s identity is important mainly for security reasons, but it could be used to obtain quick access to medical, criminal, or any type of records Solving this problem is important because it could allow people to take preventive action, provide better service in the case of a doctor appointment, allow users access to a secure area, and so forth
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