Similarity of Inference Face Matching On Angle Oriented Face Recognition

Face recognition is one of the wide applications of image processing technique. In this paper complete image of face recognition algorithm is proposed. In the prepared algorithm the local information is extracted using angle oriented discrete cosine transforms and invokes certain normalization techniques. To increase the Reliability of the Face detection process, neighborhood pixel information is incorporated into the proposed method. Discrete Cosine Transform (DCT) are renowned methods are implementing in the field of access control and security are utilizing the feature extraction capabilities. But these algorithms have certain limitations like poor discriminatory power and disability to handle large computational load. The face matching classification for the proposed system is done using various distance measure methods like Euclidean Distance, Manhattan Distance and Cosine Distance methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on image database which is acquired under variable illumination and facial expressions. It is observed from the results that use of face matching like various method gives a recognition rate are high while comparing other methods. Also this study analyzes and compares the obtained results from the proposed Angle oriented face recognition with threshold based face detector to show the level of robustness using texture features in the proposed face detector. It was verified that a face recognition based on textual features can lead to an efficient and more reliable face detection method compared with KLT (Karhunen Loeve Transform), a threshold face detector. Keywords: Angle Oriented, Cosine Similarity, Discrete Cosine Transform, Euclidean Distance, Face Matching, Feature Extraction, Face Recognition, Image texture features

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods