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Face recognition systems constitute a significant proportion of robotic systems. Learning algorithms such as deep learning and machine learning provide state-of-the-art algorithms with highly improved recognition rates. A majority of these algorithms convert a face image into a feature matrix, holistic or local, matched against all the feature matrixes in gallery for recognition. However, there is...
In this paper, we propose a dual-stage face recognition method which utilized both holistic and local features-based recognition algorithms. In the first stage Principal Components Analysis (PCA) is utilized to recognize test image. If the confidence level test is passed, the recognition process will be terminated. Otherwise, the second stage where High Dimensional Local Binary Patterns (HDLBP) is...
In this paper, we introduced a face recognition algorithm which employ dual stage with SURF descriptors. We show that SURF descriptors could be used successfully in the face recognition. In the first stage, we used interest point matching and verification, using SURF interest points. If the confidence test is failed in the first stage, we proceed to the second stage, where we use 3 rotated test images...
In this paper, we study face recognition using principal component analysis (PCA) and linear discriminant analysis (LDA) under illumination variations. A modified census transform (MCT) is applied as preprocessing step to compensate illumination variations, and then PCA and LDA are employed to find lower-dimensional subspaces for face recognition. Distances between training and testing images are...
In this paper, we implemented PCA-based face recognition algorithm in an ARM CPU embedded module. We performed experiments in the embedded system using Yale face database to measure the recognition rate and processing time. We showed that the computation time has been improved much, while the recognition rate remains similar, when we employ the conversion of floating point to fixed point arithmetic...
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