Image normalization for face recognition using 3D model

Human face recognition is still a challenging task for computer-based machines mainly due to the similarity of human faces and unpredictable variations such as illumination, pose, expression, occlusion etc. A lot of algorithms have been developed to deal with these challenging issues in face recognition. One of the difficult problems is the geometric variation of face images. In some face recognition applications, only face images with uncontrolled format are available. Face images obtained from face detection stage may be directly used. Therefore, alignment or normalization is required to achieve better recognition performance. Eye detection technique is normally used to normalize face images. Invariant representations are used to improve the generalization of facial features. Feature-based and modelbased automatic face alignment can also be used to localize human faces more precisely. Illumination variation is another tough problem because human face is a 3D model and practical lighting conditions are complicated and unpredictable. Illumination normalization based on preprocessing, illumination invariant feature extraction and face modelling have been employed to reduce illumination variations or render faces under varying lighting conditions. The geometric on-the-plane rotation of a face image can be normalized by aforementioned methods. However, a face image with rotation in depth is not easily adjusted to a certain view. It is difficult to recognize faces in all poses with only training face images in limited views. One solution is to use 2D or 3D generative models to generate novel views of the face. Another method is based on the view-subspace learning. For facial expression variations,

Three-dimensional human face modeling is a very popular topic with many applications,such as facial animation, face recognition and model-based facial video communication. Therefore, how to model the facial geometry, texture intensity and illumination variation is important in computer vision and graphics. Previous works on 3D head modeling from a single face image utilized prior information on 3D head models. However, it is difficult to accurately reconstruct the 3D face model from a single face image with expression since the facial expression induces 3D face model deformation in a complex manner. The main challenge is the coupling of the neutral 3D face model and the 3D deformation due to expression, thus making the 3D model estimation from a single face image with expression very challenging. On the other hand, the illumination condition also makes the problem more difficult. In this thesis,we focus on developing a 3D face model reconstruction system including surface registration and training of 3D face models with expressional deformations as well as the estimation of the 3D neutral shape and the 3D expressional deformation from a single face image. The proposed reconstruction algorithm integrates the linear and non-linear subspace representations for a prior 3D neutral morphable model and the probabilistic manifold-based 3D expressional deformation. We incorporate the face geometry, expression deformation, texture and illumination information into the problem so that it is well constrained. The reconstructed 3D face models can also be further extended and applied to many real-world applications.

<p>This Paper proposes a new facial feature extraction approach, Wash-Hadamard Transform (WHT). This approach is based on correlation between local pixels of the face image. Its primary advantage is the simplicity of its computation. The paper compares the proposed approach, WHT, which was traditionally used in data compression with two other known approaches: the Principal Component Analysis (PCA) and the Discrete Cosine Transform (DCT) using the face database of Olivetti Research Laboratory (ORL). In spite of its simple computation, the proposed algorithm (WHT) gave very close results to those obtained by the PCA and DCT. This paper initiates the research into WHT and the family of frequency transforms and examines their suitability for feature extraction in face recognition applications.</p>

Sample diversity, representation effectiveness and robust dictionary learning for face recognition

Surveillance video face recognition with single sample per person based on 3D modeling and blurring

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.

In this paper, a person-specific facial expression recognition method which is based on Personal Facial Expression Space (PFES) is presented. The multidimensional scaling maps facial images as points in lower dimensions in PFES. It reflects personality of facial expressions as it is based on the peak instant of facial expression images of a specific person. In constructing PFES for a person, his/her whole normalized facial image is considered as a single pattern without block segmentation and differences of 2-D DCT coefficients from neutral facial image of the same person are used as features. Therefore, in the early part of the paper, separation characteristics of facial expressions in the frequency domain are analyzed using a still facial image database which consists of neutral, smile, anger, surprise and sadness facial images for each of 60 Japanese males (300 facial images). Results show that facial expression categories are well separated in the low frequency domain. PFES is constructed using multidimensional scaling by taking these low frequency domain of differences of 2-D DCT coefficients as features. On the PFES, trajectory of a facial image sequence of a person can be calculated in real time. Based on this trajectory, facial expressions can be recognized. Experimental results show the effectiveness of this method.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Image-based 3D shape reconstruction is essential for 3D modeling in recognition, virtual reality, generation of video games, 3D animation, and in the creation of 3D virtual fitting rooms - the sought-after application in this dissertation. In this work, we propose a novel reconstruction method that uses a generic 3D model that roughly controls the overall reconstruction shape which in turn, reduces the number of 2D images needed to just two (frontal and profile) for obtaining a 3D point model. The generic model consists of a massive set of 3D points that lack saliency, topological meaning, and relational interconnections. This is also true about points in the two canonical images. In this work, we propose finding a small set of interconnected ordered intrinsic salient points (control points) residing on the silhouette of the projections of the generic model onto the two image spaces, as well as finding those for the canonical images. Points on the generic model and the images are automatically given saliency with order and interconnections through loop-subdivision operating on those control points and their equivalent points on the canonical images. The transformation of the generic model into the specific object is done through morphing the points on the generic model to follow and be consistent with their equivalent points on the canonical images. The control points on the canonical images are automatically obtained using an active shape model (ASM) that modifies an average trained control point set attained using an offline training, to account for class shape variability and boundary regularization guaranteeing the control points to reside on the silhouette (boundary) of the individual. The reconstructed 3D model is furtherly refined through smoothing the reconstructed surface, while fixing local holes using a principle component analysis (PCA) decomposition. This reconstruction method is convenient, simple, efficient, and results in an average geometric error of less than 0.5% over 700 diverse body models from the CAESAR dataset. We then implement the contour-based articulated model to the personalized 3D model for pose recovery. The evolution of the model through the articulation process is captured in a video clip. 3D transformations are found and applied to the 3D model on its parts so that the projections of the 3D model 'match' those observed in the video sequence at corresponding frames. This is done by minimizing the error between the frontal projection body region points and the target points from the image for each independent moving part, with sub-resolution recovery errors. Once the personalized 3D model is obtained and the articulated model is implemented, the algorithm simulates how garments virtually appear on the reconstructed person under different poses. Different types of clothes are classified as tight or loose, where for the first type of garments, the garment is mapped onto the person's body, whereas for the second type, the mapping adjusts to accommodate the loose nature of the garment, and its fitting on the body of the reconstructed person model. This creates a virtual in-home fitting room for garment fitting, which is timely and is a needed application that would go hand in hand with online clothes shopping, allowing the trying of garments at home with changing poses before ordering them, and can be updated as often as necessary to accommodate changes in physique, weight, aging, etc. This is also done with minimum requirements from the user aside from taking two images and a video of pose articulations using their cam or smart phone, and stands as a useful, needed, and attractive App.

In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.

Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.

Principal component analysis (PCA) has been proven to be an efficient method in pattern recognition and image analysis. Recently, PCA has been extensively employed for face-recognition algorithms, such as eigenface and fisherface. The encouraging results have been reported and discussed in the literature. Many PCA-based face-recognition systems have also been developed in the last decade. However, existing PCA-based face-recognition systems are hard to scale up because of the computational cost and memory-requirement burden. To overcome this limitation, an incremental approach is usually adopted. Incremental PCA (IPCA) methods have been studied for many years in the machine-learning community. The major limitation of existing IPCA methods is that there is no guarantee on the approximation error. In view of this limitation, this paper proposes a new IPCA method based on the idea of a singular value decomposition (SVD) updating algorithm, namely an SVD updating-based IPCA (SVDU-IPCA) algorithm. In the proposed SVDU-IPCA algorithm, we have mathematically proved that the approximation error is bounded. A complexity analysis on the proposed method is also presented. Another characteristic of the proposed SVDU-IPCA algorithm is that it can be easily extended to a kernel version. The proposed method has been evaluated using available public databases, namely FERET, AR, and Yale B, and applied to existing face-recognition algorithms. Experimental results show that the difference of the average recognition accuracy between the proposed incremental method and the batch-mode method is less than 1%. This implies that the proposed SVDU-IPCA method gives a close approximation to the batch-mode PCA method.

With the improvement of security requirements of individuals, businesses and countries, the identity authentication system based on face recognition technology is applied widely. The identity methods based on single theory, due to a variety of factors, have their own inherent limitations. In order to improve the efficiency and accuracy of face recognition, based on fusion of wavelet packet sub-images, a face recognition algorithm named FW-FLD is proposed in this paper by combining PCA (Principal Component Analysis) and FLD(Fisher Linear Discriminant). Firstly, the training samples are decomposed using wavelet packet. The fusion weights of the decomposed sub-images are calculated according to their energy distribution and the weighted fusion images are obtained, which reserve the images characteristic in the frequency. Then the features of the fused images are extracted using PCA, and the optimized Fisher space is constructed using FLD. Finally, face images are classified by measuring the projection coefficients of optimized training samples and testing samples in Fisher space. Experimental results on CMU PIE, JAFFE and AR face databases show that the proposed algorithm is robust, and can adapt to face recognition with various illumination, facial expressions and gestures. Compared with other algorithms, it not only improves the face recognition rate, but also has a higher time efficiency.

We propose a three-dimensional (3D) face-modelling method from a single two-dimensional (2D) face image using a gallery of 2D face images and their corresponding 3D face models. Unlike existing methods, which require human effort, we provide a simple way to reconstruct 3D face models without user interaction. Our main approach is based on the idea that a particular coefficient that linearly combines vectors of 2D face images and outputs a vector that approximates the input image vector in terms of the vector norm can be reused in 3D models. Therefore, the pair of a 2D image and its 3D model plays an important role in our algorithm. Using the FaceGen software allows us to avoid the employed in previous works procedure whereby the 3D model is generated in a labor-intensive, expensive way. As a result, we are able to easily establish our 2D and 3D database. In this paper, we present a method for adopting the coefficients in 3D models and demonstrate the results of our algorithm.

In this paper we present a system which automatically generates a 3D face model from a single frontal image of a face with the help of generic 3D model. Our system consists of three components. The first component detects the features like eyes, mouth, eyebrow and contour of face. After detecting features the second component automatically adapts the generic 3d model into face specific 3D model using geometric transformations. Our system allows the rotation and zooming of 3D model and generation of texture. Animation is produced using 3D shape morphing between the corresponding face models and blending the corresponding textures. Our system has the advantage that it is fully automatic, robust and fast. It can be used in a variety of applications for which the accuracy of depths are not critical such as games, avatars, face recognition etc. We have tested and evaluated our system using database BU-3DFE.