Gradient-Orientation-Based PCA Subspace for Novel Face Recognition

Abstract

Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.

Face Recognition System based on SURF and LDA Technique

“…In the Table 1, we have calculated the error rate in four parts and compared to proposed method and previous method. In previous work, five methods were used name as Eigenfaces, Laplacianfaces, Fisherfaces, FDS and Schurfaces, these method were used for minimize the error rate [11].In the previous methodology, Schurfaces got the minimum error rate 14%, But in our methodology, we have used SURF and Linear Discriminant Analysis to find the results and find minimum error in all four parts. By using LDA methodology, our results are better; in the " Fig.…”

“…The PCA based feature reused to increase the accuracy of the system. Data without feature selection may be redundant or noisy which may degrade classification accuracy rate [9], [11]. David Barina presented the use of twodimensional Gabor wavelets in image processing.…”

Real time automated facial expression recognition app development on smart phones

“…The BRIEF descriptor [36] and HOG descriptor have been examined for use in FER as well [37], [38], [39]. In this study, we evaluate the performance of a relatively new feature descriptor called the facial landmarks descriptor [20] in facial expression recognition. To the best of our knowledge, we are the first to consider the facial landmarks descriptor in real time FER development on a mobile phone platform.…”

“…In our research, the Facial Landmarks method was used to extract features following the pre-processing step, which detected and subsequently tracked facial movement with the Hausdorff Distance method of detection [20]. Feature extraction and classification were the essential basis of the method.…”

Impact of Face Partitioning On Face Recognition Performance

“…al. [7] first made an attempt in integrating the Hausdorff Distance (HD) and Schur decomposition for dimensionality reduction based face recognition. The Schur faces have the high discriminative power and performed well over the standard face recognition methods.…”