Age simulation for face recognition

In recent days, there has been increasing need for recognition of unconstrained face images, such as those collected from the web or captured by mobile devices and video surveillance cameras. In such real world scenarios, human faces could be easily occluded by other objects that make the face recognition task as a complex one. Satisfactory performance has been achieved earlier but often only in the controlled environments. But it is very tedious to obtain holistic face images for unconstrained face recognition. Thus, in order to avoid the degradation of face images and the huge variations due to illumination, pose, occlusion and expression, a new Robust Face Recognition approach is proposed. In this approach, the partial face recognition using Scale Invariant Feature Transform (SIFT) technique is combined with Multi-directional Multi-level Dual Cross Patterns (DCP) technique that makes the recognition task as a robust one when compared to other face recognition approaches. Then, the Robust Point Set Matching (RPSM) is used to match the corresponding stable keypoints from both the gallery image and probe face image. Finally, PNN and K-NN classification are used to classify the face images even with the presence of occlusion, random partial crop, illumination, pose and exaggerated facial expression. The proposed robust face recognition system is evaluated based on the performance parameters such as sensitivity, specificity, accuracy, precision and recall.

Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition.

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear Support Vector Machine Classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.

Machine learning and pattern recognition recently become a hot topic in computing world. This is due to the fast-growing of resources as well as techniques that make it easier to solve machine learning and pattern recognition problems. Problems that require machine learning solutions may be very simple for humans but actually can be very complex for machines to solve them. Face recognition is amongst those problems. Almost all human can easily recognize others without require specific knowledge to do it, different from machines which require its. This paper discussed face recognition task using machine learning strategies which involved Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to identify person. KPCA extracted features from 2D image input and produced the important features of an image input. The extracted face features are recognized by SVM by classifying human face according to their stored identity in a database. SVM, which was basically a binary classifier worked by using one-against-one strategy to compare the face feature vector of a single test image to the stored face image in a face image database. Experiment results on grayscale images with size 92x112 pixels gave 96.25% of accuracy rate. Hence, KPCA and SVM for face recognition is a robust machine learning method.

Based on sub-image feature extraction method of face images and least square support vector machine (LS-SVM) classifier, a face recognition model is proposed. In this model, face images are divided into sub-images firstly, then the discrete cosine transform is operated on each sub-image and the optimal coefficients are selected to denote the sub-image, and combine them into a single feature vector of the whole face image, finally LS-SVM is employed as a classifier with its optimized parameters. On ORL face database, experimental results show the proposed method is effective and has fast recognition speed.

This study proposes a face recognition model using a combination of shape and texture vectors that are used to produce new face images on 2D-3D reconstruction images. The reconstruction process to produce 3D face images is carried out using the convolutional neural network (CNN) method on 2D face images. Merging shapes and textures vector is used to produce correlation points on new face images that have similarities to the initial image used. Principal Component Analysis (PCA) is used as a feature extraction method, for the classification method we use the Mahalanobis method. The results of the tests can produce a better recognition rate compared to face recognition testing using 2D images.

Aiming at the problem of low recognition rate caused by local deformations such as pose, angle, expression change and occlusion, especially small local deformations in face recognition. A face recognition algorithm based on Gabor wavelet transform and central symmetric local direction pattern (CSLDP) is proposed. Gabor is used to extract multi-dimensional features, and CSLDP is used to reduce the computational complexity. Then histogram feature information is divided into blocks and counted in series as feature vectors of face images. Finally, sparse representation classifier (SRC) is used for classification and recognition. The experimental results on ORL, YALE and AR face databases show that the proposed method can adapt to the changes of fine expressions and light, and has strong robustness.

A face recognition method based on joint sparse representation of multiple features is proposed in this paper. First, principle component analysis (PCA), kernel PCA (KPCA), and non-negative matrix factorization (NMF) are used to extract feature vectors of face images. The three features could provide complementary descriptions for face images. Then, in the classification stage, joint sparse representation is employed to classify the three features thus considering their correlations. Finally, the total reconstruction errors of the three features on different kinds of training classes are calculated to determine the label of test sample. Experiments are conducted on AR and Yale-B databases to validate the effectiveness of the proposed method.

다양한 조명 환경 하에서, 얼굴인식이 잘 동작하도록 하는 것은 매우 어려운 일이며 성공적인 상업화를 위해서는 반드시 성취되어야 하는 작업이다. 본 논문에서는 얼굴 인식을 위한 효율적인 조명 전처리 방법을 제안한다. Anisotropic smoothing 기반 조명 전처리 방법은 조명 전처리 방법 가운데 효과적인 방법으로 잘 알려져 있으나, 원 이미지의 명도 대비를 감소시키며 에지 성분의 약화를 초래한다. 본 논문의 제안 방법은 기존 anisotropic smoothing 방법을 개선하여, 조명의 영향을 줄이면서 명도 대비를 증가시키고 에지 정보를 강화한다. 이러한 개선의 결과로, 본 논문의 제안 방법에 의해 조명 전처리된 같은 사람의 얼굴 이미지들은 보다 차별적인 특징 벡터(가버 특징 벡터)를 갖게 된다. 본 논문에서 제안한 조명 전처리 방법의 효율성은 가버젯 유사도를 이용한 얼굴 인식의 실험을 통하여 입증되었다. Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an efficient illumination preprocessing method for face recognition. illumination preprocessing algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing effects of illumination. Due to the result of these improvements, face images preprocessed by the proposed illumination preprocessing method becomes to have more distinctive feature vectors(Gabor feature vectors). Through experiments of face recognition using Gabor jet similarity, the effectiveness of the proposed illumination preprocessing method is verified.

In this study, a new method is presented for face recognition by using DCT4-faces that are calculated by the 4D discrete cosine transform. In this method, firstly, 3D struct is obtained for each person by collecting training face images on top of each other. Then 4D DCT is applied to 4D struct which is obtained from 3D structs of each person. Lasty, 2D inverse DCT is applied to each 2D coefficient layer separately. After this operation, DCT4-faces are found. By taking the inner product of DCT4-faces and original face images, feature vectors are calculated to be used in face recognition. The main difference of the presented method from other DCT-based face recognition methods is that DCT4-faces does not directly use DCT coefficients as feature vector.

This paper proposes a content-based authentication scheme for tampering detection and localization of binary images. The watermark is generated by the feature vector of the original binary image, and embedded back into the image by a structural method. The feature vector we used is Zernike moments. It can tell the degree of the tamper in the binary image. A distance between two feature vectors is defined. We authenticate image by the distance between the extracted watermark and the feature vector of the test image. Once the tampering is detected, we can locate the tampered areas by comparing different components of the distance. The experimental results show that the algorithm can locate the tamper effectively.

Face recognition is a process by which the identity of a person is determined from the face images stored in a face database. Face recognition is one of the most successful applications of image analysis. In the present scenario, face recognition plays a major role in commercial and law enforcement applications, such as surveillance system, passport, security, personal information accesses, human machine interaction, etc. At present, very reliable methods of biometric personal identification exist. In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. This chapter addresses this issue by means of image fusion and presents a comprehensive study of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images.

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.