A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.

Normalization of Face Illumination Based on Large-and Small-Scale Features

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Recent Advances in Face Recognition

This letter introduces a design and proof-of-concept implementation of Gabor filters based on stochastic computation for area-efficient hardware. The Gabor filter exhibits a powerful image feature extraction capability, but it requires significant computational power. Using stochastic computation, a sine function used in the Gabor filter is approximated by exploiting several stochastic tanh functions designed based on a state machine. A stochastic Gabor filter realized using the stochastic sine shaper and a stochastic exponential function is simulated and compared with the original Gabor filter that shows almost equivalent behaviour at various frequencies and variance. A root-mean-square error of 0.043 at most is observed. In order to reduce long latency due to stochastic computation, 68 parallel stochastic Gabor filters are implemented in Silterra $0.13~\mu\hbox{m}$ CMOS technology. As a result, the proposed Gabor filters achieve a 78% area reduction compared with a conventional Gabor filter while maintaining the comparable speed.

Gabor Filter Based on Stochastic Computation

Expression detection is useful as a non-invasive method of lie detection and behaviour prediction. However, these facial expressions may be difficult to detect to the untrained eye. In this paper we im plements facial expression recognition techniques using Principal Component analysis (PCA). Experiments are performed using standard database like Japanese Female Facial Expression (JAFFE) database. The universally accepted six principal emotions to be recognized are: Angry, Happy, Sad, Disgust, Fear and Surprise along with neutral. Euclidean distance based matching Classifier is used.

Facial Expression Recognition Using Principal Component Analysis

In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set. The modified AAM search can now handle both illumination and pose variations in the same epoch, hence it provides better convergence in both point-to-point and point-to-curve senses. We also investigate how face recognition performance is affected by the selection of feature space as well as the proposed alignment method. The experimental results show that the combined pose alignment and illumination normalization methods increase the recognition rates considerably for all feature-spaces.

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Robust Face Alignment for Illumination and Pose Invariant Face Recognition

An illumination normalization model for face recognition under varied lighting conditions

A discriminant feature fusion model is proposed for face recognition with large variations of pose, expression, lighting, etc. Discriminant features are extracted by the wavelet transform-based method from two source images. One source image is a holistic gray value image and the other is an illumination invariant geometric image. Face sample is reconstructed by the adaptive fused discriminant feature. Then a bank of Gabor filters is built to extract Gabor representations of the reconstructed samples. Finally higher-order statistical relationships among variables of samples are extracted for classifier. According to experiments, the model outperforms conventional algorithms under complex conditions (large variations of lighting, expression, accessory, etc.).

Independent Gabor Analysis of Discriminant Features Fusion for Face Recognition

The invention discloses a face recognition method by an independent component analysis based on a multi-scale total variational derivative image, which belongs to the face recognition technical field; the method is as follows: a contrast gradient is strengthened; TV-L is used to carry out scale decomposition to a face image to obtain a large-scale image comprising a skeleton contour and muscle information and a small-scale image comprising the details of mouth, eyes and nose; quotient balance is carried out to the small-scale image to obtain the feature of unchanged illumination; feature fusion technology is selected to fuse the features of large scale and unchanged illumination into a new face image; Gabor is used to analyze and extract the features of the new face image in a specific scale and direction to generate a multi-scale Gabor face; the eigenvectors of all the samples are extracted by an information maximization independent component analysis algorithm; the similarity of the eigenvectors of the face which is to be treated with recognizing is calculated by the eigenvectors of the known face; according to the similarity, the eigenvectors are sorted to acquire a final recognition result. The face recognition method achieves high recognition rate and strong robustness to illumination, expression, make-up and other external interference.

Independent component analysis human face recognition method based on multi- scale total variation based quotient image

To suitably complete an image without seams, block effects, and artifacts, a novel exemplar-based image completion model employing partial differential equation (PDE) is proposed. Firstly, the novel model determines the processing order of exemplar according to a composite function, which is the product of the colour property and structure property in exemplar. Then the exemplar along a geometric structure is processed prior to other parts of the image. Secondly, the most similar exemplar is found in the CIELAB colour space, and the size of it is adaptively determined by the local textured information. Thirdly, a Poisson equation is adopted to remove the seams, block effects, and artifacts in the image generated by the exemplar-based model. Finally, a bi-directional diffusion PDE is used to assist the completion of lathy linear structure. Experimental results demonstrate that the novel model can properly reconstruct the target region while preserving the geometric structure without inducing block effects, which leads to its better performance than the conventional exemplar-based image completion models.

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Exemplar-Based Image Completion Model Employing PDE Corrections

The invention relates to a module-based image inpainting method. The method comprises the following steps: selecting a target area to be inpainted; calculating statistical values of textural features of modules to be inpainted which have preset size and correspond to pixel points in the target area to be inpainted, and adjusting the sizes of the modules to be inpainted according to the calculated statistical values of the textural features; calculating priority levels of the modules to be inpainted by means of confidence level constraint and data item constraint; searching the module which is most similar to the module to be inpainted with the highest priority in a known image area, filling all the pixel points in the area into the module to be inpainted, and meanwhile updating the confidence level constraint of pixels filled in the module to be inpainted at this time; reducing seam effect of the filled module to be inpainted by utilizing an overlap region image fusion technology under divergence constraint; and repeating the steps in the filled target area to be inpainted until all the pixels in the target area to be inpainted are filled.

Jiying Wu

Papers

An illumination normalization model for face recognition under varied lighting conditions

Independent Gabor Analysis of Discriminant Features Fusion for Face Recognition

Independent component analysis human face recognition method based on multi- scale total variation based quotient image

Exemplar-Based Image Completion Model Employing PDE Corrections

Module-based image inpainting method