Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement

Problem statement: Face occlusion is a very challenging problem in fac e recognition. The performance of face recognition system decreases dr astically due to the presence of partial occlusion on the face. Extracting discriminative features to achieve accurate detection versus computational overhead in extracting the features, which affects the classification speed, would be a sustained problem. The objective of this study is to segment the human face into non-occluded and occluded part of the occluded human face image. In General, for f ace detection special facial features are extracted . In the proposed study a simplified algorithm to ext ract the features is developed. Approach: An algorithm which enables the automatic detection of the presence of occlusions on the face would be a useful tool to increase the performances of the sys tem. The face image was preprocessed to enhance the input face images in order to reduce the loss o f classification performance due to changes in faci al appearance. The experiment also balances both illumination and facial expression changes. Results: In this study, a Mean Based Weight Matrix (MBWM) algorithm has been proposed to enhance the performance by 4.25% than the LBP method. Conclusion: The proposed model has been tested on occluded face images with a dataset obtained from t he MIT face database.

Illumination variations significantly affect the performance of the automatic face recognition system. To achieve optimum contrast enhancement, contrast limiting adaptive histogram equalization (CLAHE) has been used in this work. Histogram Equalization (HE) modifies the histogram of the image globally based on intensity distribution of an entire image. However, the feature of interest in an image needs enhancement locally. CLAHE is based on the intensity distribution in a neighborhood of every pixel in the image. Further, for removing the illumination variations in the face image, the appropriate number of low frequency DCT coefficients has been scaled down as illumination variations mainly lie in the low-frequency band. After eliminating illumination variations effect, mapping of the data on to another feature space is done using Kernel PCA (KPCA), which extract higher order statistics. KPCA has the advantage of less computation time and improve performance level. Classification is done by using nearest neighbor classifier. Experiments are performed on Extended Yale B database. The experimental results show that the performance of our method is significantly better than that of any existing state-of-art technique.

The panorama of face recognition is to find the dissimilarity features that can be used to discriminate individuals for their recognition. The holistic face recognition methods available in the literature perform well in the controlled environments. These methods may not appropriate to identify people through their faces in uncontrolled environments such as changes in pose, facial expression and illumination. However, the feature based methods address the issues of face recognition in uncontrolled environments to a certain extent. Therefore, the biometric researchers are trying to devise the face recognition methods that perform optimally in uncontrolled environments. This paper presents a novel method of face recognition in uncontrolled environments that works with local binary pattern of facial images and compute the dissimilarity among them using Bray Curtis dissimilarity metric. A novel concept of filtering the LBP surface texture is developed. The proposed method we called as "augmented local binary" pattern ((ALBP)) works on a combination of the principle of locality of uniform and non-uniform patterns. It replaces non-uniform patterns with the majority value of uniform patterns and combined with neighboring uniform patterns to extract valuable information regarding local descriptors. The proposed method is tested on different databases consisting uncontrolled facial images such as extended Yale B, Yale A and our database. The experimental results show that the proposed method performs better at recognizing faces in uncontrolled environments such as the facial expression, illumination, and mild pose changes.

At present, the performance of face recognition system depends much on the variations in illumination. To solve this problem, this paper presents an adaptable face recognition approach that uses filter fusion representation. The key idea is to use context-aware filter fusion to get better image from a bad illumination one. Genetic algorithm is the tool for adaptation for individual context category. These can provide robust face recognition on illumination context-awareness under uneven environments. Gabor wavelet representation can also provide a robust feature for image enhancement. Using these approaches, we have developed a robust face recognition technique that can recognize with a notable success and it has been tested on Inha DB and FERET face images.

Illumination, pose variations, disguises, aging effects and expression variations are some of the key factors that affect the performance of face recognition systems. Face recognition systems have always been studied from a recognition perspective. Our emphasis is on deriving a measure of similarity between faces. The similarity measure provides insights into the role each of the above mentioned variations play in affecting the performance of face recognition systems. In the process of computing the similarity measure between faces, we suggest a framework to compensate for pose variations and introduce the notion of 'half-faces' to circumvent the problem of non-uniform illumination. We used the similarity measure to retrieve similar faces from a database containing multiple images of individuals. Moreover, we devised experiments to study the effect age plays in affecting facial similarity. In conclusion, the similarity measure helps in studying the significance facial features play in affecting the performance of face recognition systems.

An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem of pose variation and illumination change for face recognition is proposed. The system has combined 2D and 3D systems in the feature level which presents higher performance in contrast with methods which utilizes either 2D or 3D system separately. A multimodal face recognition based on Modular PCA when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in illumination and facial expression. The proposed algorithm is tested with FRAV3D database that has faces with pose variation and illumination changes. Recognition rates from experimental results show the superiority of Modular PCA over conventional PCA methods in tackling face images with different pose variations and changes in illuminations. The proposed algorithm shows a recognition rate of 86% that is achieved in fusion experiment.

The performance of face recognition system is greatly affected by the illumination changes. In this article, we propose a method of face illumination compensation based on neural network and wavelet. It sufficiently combines multi-resolution analysis of wavelet and the self-adaptation learning and good spread ability of BP neural network, thus this method carries out the face illumination compensation. The theory and experiments prove that this method solves the problem of illumination compensation efficiently in the face detection and recognition process. It improves the face detection and recognition under different illumination conditions. Moreover, it has good robustness and can be used in a wide range.KeywordsNeural networkWavelet analyzeIllumination compensationFace recognitionFace detection

The pose variation involved in facial images significantly degrades the performance of face recognition systems. In this paper, a component-wise pose normalization method for facilitating pose-invariant face recognition is proposed. The main idea is to normalize a non-frontal facial image to a virtual frontal image component by component. In this method, we first partition the whole non-frontal facial image into different facial components and then the virtual frontal view for each component is estimated separately. The final virtual frontal image is generated by integrating the virtual frontal components. The proposed method relies only on 2D images, therefore complex 3D modeling is not needed. The experimental results using the CMU-PIE database demonstrate the advantages of the proposed method.

Human facial expressions describe a set of signals, which can be associated with mental states such as emotions depending on physiological conditions. There are many potential applications of expression recognition systems. They take into account about two hundred emotional states. Expression recognition is a challenging problem, not only due to the variety of expressions, but also due to difficulty in extraction of effective features from facial images. Depending on a 3D reconstruction technique, 3D data can be immune to a great range of illumination and texture variations, and they are no sensitive as 2D images to out-of-plane rotations. Moreover, 2D images may fail to capture subtle but discriminative change on the face if there is no sufficient change in brightness, such as bulges on the cheeks and protrusion of the lips. In fact, 3D data yield better recognition than conventional 2D data for many types of facial actions The most effective tool for solution of the problem of human face recognition is neural networks. But the result of recognition can be spoiled by facial expressions and other deviation from canonical face representation. In the proposed presentation we describe a resampling method of human faces represented by 3D point clouds. The method based on the non-rigid ICP (Iterative Closest Point) algorithm. We consider the combined using of this method and convolutional neural network (CNN) in the face recognition task. Computer simulation results are provided to illustrate the performance of the proposed algorithm.

Face aging simulation and recognition based on NMF algorithm with sparseness constraints

Face recognition at a distance is one grand challenge for security surveillance. In this paper, the face images at different distances are simulated by varying image scales (resolutions). The performances of three face recognition algorithms (matchers) are tested with variant image scales (simulating different distances) and with two spectral images (modalities). The three selected matchers are face pattern byte, elastic bunch graph matching, and linear discriminant analysis; while the two modalities are visible and thermal images. The performance of a face recognition system can be measured by accuracy (AC) rate and false accept rate (FAR). To enhance the performance of face recognition especially at a distance, score fusion techniques are applied, which combine several scores from multiple matchers and multiple modalities. Our experiments are conducted with the ASUMS face dataset consisting of two spectral images (visible and thermal) from 135 subjects. The experimental results show that the face recognition with small image scales (simulating long distances) have low performance (e.g., AC=91.36%, FAR=8.64% for 20×20-pixel images); and score fusion can greatly improve accuracy (99.34%) meanwhile reduce FAR (0.31%).

The Enhanced Border Security and Visa Entry Reform Act of 2002 requires that the Visa Waiver Program be available only to countries that have a program to issue to their nationals machine-readable passports incorporating biometric identifiers complying with applicable standards established by the International Civil Aviation Organization (ICAO). In June 2002, the New Technologies Working Group of ICAO unanimously endorsed the use of face recognition (FR) as the globally interoperable biometric for machine-assisted identity confirmation with machine-readable travel documents (MRTDs), although Member States may elect to use fingerprint and/or iris recognition as additional biometric technologies. The means and formats are still being developed through which biometric information might be stored in the constrained space of integrated circuit chips embedded within travel documents. Such information will be stored in an open, yet unalterable and very compact format, probably as digitally signed and efficiently compressed images. The objective of this research is to characterize the many factors that affect FR system performance with respect to the legislated mandates concerning FR. A photograph acquisition environment and a commercial face recognition system have been installed at Mitretek, and over 1,400 images have been collected of volunteers. The image database and FR system are being used to analyze the effects of lossy image compression, individual differences, such as eyeglasses and facial hair, and the acquisition environment on FR system performance. Images are compressed by varying ratios using JPEG2000 to determine the trade-off points between recognition accuracy and compression ratio. The various acquisition factors that contribute to differences in FR system performance among individuals are also being measured. The results of this study will be used to refine and test efficient face image interchange standards that ensure highly accurate recognition, both for automated FR systems and human inspectors. Working within the M1-Biometrics Technical Committee of the InterNational Committee for Information Technology Standards (INCITS) organization, a standard face image format will be tested and submitted to organizations such as ICAO.

The performance of face recognition systems that use two-dimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. We are developing a multi-view face recognition system that utilizes three-dimensional (3D) information about the face to make the system more robust to these variations. This work describes a procedure for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views, using coordinate system invariant properties of the facial surface. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. A robust similarity metric is defined for matching, based on an iterative closest point (ICP) registration process. Results are given for matching a database of 18 3D face models with 113 2.5D face scans.

Face images obtained by an outdoor surveillance camera, are often confronted with severe degradations (e.g., low-resolution, low-contrast, blur and noise). This significantly limits the performance of face recognition (FR) systems. This paper presents a framework to overcome the degradation in images obtained by an outdoor surveillance camera, to improve the performance of FR. We have defined a measure that is based on the difference in intensity histograms of face images, to estimate the amount of degradation. In the past, super-resolution techniques have been proposed to increase the image resolution for face recognition. In this work, we attempt a combination of partial restoration (using super-resolution, interpolation etc.) of probe samples (long distance shots of outdoor) and simulated degradation of gallery samples (indoor shots). Due to the unavailability of any benchmark face database with gallery and probe images, we have built our own database and conducted experiments on a realistic surveillance face database. PCA and FLDA have been used as baseline face recognition classifiers. The aim is to illustrate the effectiveness of our proposed method of compensating the degradation in surveillance data, rather than designing a specific classifier space suited for degraded test probes. The efficiency of the method is shown by improvement in the face classification accuracy, while comparing results obtained separately using training with acquired indoor gallery samples and then testing with the outdoor probes.KeywordsDegradationFace databaseFace recognitionImage qualitySurveillanceSuper-resolution

The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Due to that both Gabor feature and LTP(local ternary pattern) are testified to be robust to illumination variations, we proposed a new approach which achieved illumination variable face recognition by combining Gabor filters with LTP operator. The experimental results compared with the published results on Yale-B and CMU PIE face database of changing illumination verify the validity of the proposed method.