[Show abstract][Hide abstract] ABSTRACT: Face recognition algorithms can be divided into two categories: holistic and local feature-based approaches. Holistic methods are very popular in recent years due to their good performance and high efficiency. However, they depend on careful positioning of the face images into the same canonical pose, which is not an easy task. On the contrary, some local feature-based approaches can achieve good recognition performances without additional alignment. But their computational burden is much heavier than holistic approaches. To solve these problems in holistic and local feature-based approaches, we propose a fully automatic face recognition framework based on both the local and global features. In this work, we propose to align the input face images using multi-scale local features for the holistic approach, which serves as a filter to narrow down the database for further fine matching. The computationally heavy local feature-based approach is then applied on the narrowed database. This fully automatic framework not only speeds up the local feature-based approach, but also improves the recognition accuracy comparing with the holistic and local approaches as shown in the experiments.
[Show abstract][Hide abstract] ABSTRACT: Many face recognition algorithms depend on careful positioning of face images into the same canonical pose. Currently, this positioning is usually done by detecting the locations of eyes. And the face images are transformed to the same positions according to the eye coordinates detected. In this paper, we describe a method based on multi-scale local features to achieve face alignment automatically not just dependent on the localizations of two eyes. Given an unaligned face image resulting from a face detector and a set of aligned face images in the data set, we build an automatic transformation mechanism, under which the unaligned face image can be precisely aligned for the following recognition process. Our alignment method improves performance on face recognition tasks, over images aligned by many other algorithms.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a framework of face recognition based on the multi-scale local structures of the face image. While some basic tools in this framework are inherited from the SIFT algorithm, this work investigates and contributes to all major steps in the feature extraction and image matching. New approaches to keypoint detection, partial descriptor and insignificant keypoint removal are proposed specifically for human face images, a type of non-rigid and smooth visual objects. A strategy of keypoint search for the nearest subject and a two-stage image matching scheme are developed for the face identification task. They circumvent the problem that local structures matched with those in probe disperse into many different gallery images. Although the proposed framework can work for single template per subject, a training procedure is developed for multiple samples per subject. It contains template selection, unstable keypoint removal and template synthesis to meet different requirements in face recognition applications. Each ingredient of the proposed framework is experimentally validated and compared with its counterpart in the SIFT scheme. Results show that the proposed framework outperforms SIFT and some holistic approaches to face recognition.
Full-text · Article · Oct 2011 · Pattern Recognition
[Show abstract][Hide abstract] ABSTRACT: Scale Invariant Feature Transform (SIFT) has shown to be a powerful technique for general object recognition/detection. In this paper, we propose two new approaches: Volume-SIFT (VSIFT) and Partial-Descriptor-SIFT (PDSIFT) for face recognition based on the original SIFT algorithm. We compare holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT and PDSIFT. Experiments on the ORL and AR databases show that the performance of PDSIFT is significantly better than the original SIFT approach. Moreover, PDSIFT can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.
[Show abstract][Hide abstract] ABSTRACT: Scale Invariant Feature Transform (SIFT) has shown to be very powerful for general object detection/recognition. And recently, it has been applied in face recognition. However, the original SIFT algorithm may not be optimal for analyzing face images. In this paper, we analyze the performance of SIFT and study its deficiencies when applied to face recognition. We propose two new approaches: Keypoints-Preserving-SIFT (KPSIFT) which keeps all the initial keypoints as features and Partial-Descriptor-SIFT (PDSIFT) where keypoints detected at large scale and near face boundaries are described by a partial descriptor. Furthermore, we compare the performances of holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT, KPSIFT and PDSIFT. Experimental results on ORL and AR databases show that our proposed approaches KPSIFT and PDSIFT can achieve better performance than the original SIFT. Moreover, the performance of PDSIFT is significantly better than FLDA and NLDA. And PDSIFT can achieve the same or better performance than the most successful holistic approach ERE.