Abstract
Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar Harmonic Transforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.
Highlights
Face recognition is an important process for facial emotion recognition, face tracking, gender classification, multimedia applications, automatic face recognition, and many others [1,2]
Tab. 5 represents the comparison of the proposed face detection system with Viola-Jones, LBP, and Multi-Block Local Binary Pattern (MBLBP)
It has been verified that the proposed face detection system can detect the face at different image resolution like 115 × 115, 82 × 82, 105 × 105, 250 × 250, 92 × 118, 128 × 120, 512 × 512, 106 × 49, 64 × 64, 1024 × 1024 and 256 × 256 and with different facial expressions and emotions, and with different resolutions
Summary
Face recognition is an important process for facial emotion recognition, face tracking, gender classification, multimedia applications, automatic face recognition, and many others [1,2]. The rotated or tilted image recognition is a great challenge during authentication and pattern recognition system. When a photo is taken through the camera, it may detect the face and create a rectangle shape with different angles and poses. This is due to pose variations, lighting conditions, and rotations of a camera during the shooting. Authors in [7] stated that most recognition algorithms might degrade 10% in face verification, indicating that the pose variation remains a significant challenge in face recognition. Authors in [8] suggested thinking about feature representation invariant to pose in recognizing in surveillance videos
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