https://imaging.utk.edu/publications/papers/2005/kong_cviu05.pdf

Recent advances in visual and infrared face recognition: a review

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

/pdf/a-face-and-palmprint-recognition-approach-based-on-26b4ehubou.pdf

A face and palmprint recognition approach based on discriminant DCT feature extraction

This paper describes a new software-based registration and fusion of visible and thermal infrared (IR) image data for face recognition in challenging operating environments that involve illumination variations. The combined use of visible and thermal IR imaging sensors offers a viable means for improving the performance of face recognition techniques based on a single imaging modality. Despite successes in indoor access control applications, imaging in the visible spectrum demonstrates difficulties in recognizing the faces in varying illumination conditions. Thermal IR sensors measure energy radiations from the object, which is less sensitive to illumination changes, and are even operable in darkness. However, thermal images do not provide high-resolution data. Data fusion of visible and thermal images can produce face images robust to illumination variations. However, thermal face images with eyeglasses may fail to provide useful information around the eyes since glass blocks a large portion of thermal energy. In this paper, eyeglass regions are detected using an ellipse fitting method, and replaced with eye template patterns to preserve the details useful for face recognition in the fused image. Software registration of images replaces a special-purpose imaging sensor assembly and produces co-registered image pairs at a reasonable cost for large-scale deployment. Face recognition techniques using visible, thermal IR, and data-fused visible-thermal images are compared using a commercial face recognition software (FaceIt®) and two visible-thermal face image databases (the NIST/Equinox and the UTK-IRIS databases). The proposed multiscale data-fusion technique improved the recognition accuracy under a wide range of illumination changes. Experimental results showed that the eyeglass replacement increased the number of correct first match subjects by 85% (NIST/Equinox) and 67% (UTK-IRIS).

https://imaging.utk.edu/publications/papers/2006/241_IJCV_Kong07.pdf

Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition

http://www.cbsr.ia.ac.cn/publications/ytian/PR.pdf

Do singular values contain adequate information for face recognition

A personalized feature combination scheme is proposed for face verification. ANFIS (adaptive neuro-fuzzy inference system) and SVM (support vector machine) are adopted respectively to form specialized feature representation for each subject by fusing global and local features. Instead of the common way for different subjects, we realize a new representation that adapts to each individual. Such adaptability in feature selection is inspired by face recognition in the HVS (human visual system) and results in an improved recognition rate.

/pdf/fusion-of-global-and-local-features-for-face-verification-1tn2pb6ywu.pdf

Fusion of global and local features for face verification

In this paper, we propose a novel contour-region face detector based on the modified committee method. The detector fuses results from a face-contour-based classifier and a facial-area-based classifier, both of which are a SVM (Support Vector Machine). The former is used to identify face contour pattern, and the latter is aimed at discriminating facial area and non-facial area. A skin-color filter is adopted to accelerate the detection. Through serial and parallel fusion of multiple cues, a robust face detection algorithm is implemented through the complementarity of color, contour and region.

/pdf/combining-color-contour-and-region-for-face-detection-11apv2u347.pdf

Yuchun Fang

Papers

Do singular values contain adequate information for face recognition

Fusion of global and local features for face verification

Combining Color, Contour and Region for Face Detection

Enhanced task attention with adversarial learning for dynamic multi-task CNN

Adversarial multi-task deep learning for signer-independent feature representation