Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Efficient 3D Face Recognition with Gabor Patched Spectral Regression

In this paper, we utilize a novel framework for 3D face recognition, called 3D Gabor Patched Spectral Regression (3D GPSR), which can overcome some of the continuing challenges encountered with 2D or 3D facial images. In this active field, some obstacles, like expression variations, pose correction and data noise deteriorate the performance significantly. Our proposed system addresses these problems by first extracting the main facial area to remove irrelevant information corresponding to shoulders and necks. Pose correction is used to minimize the influence of large pose variations and then the normalized depth and gray images can be obtained. Due to better time-frequency characteristics and a distinctive biological background, the Gabor feature is extracted on depth images, known as 3D Gabor faces. Data noise is mainly caused by distorted meshes, varieties of subordinates and misalignment. To solve these problems, we introduce a Patched Spectral Regression strategy, which can make good use of the robustness and efficiency of accurate patched discriminant low-dimension features and minimize the effect of noise term. Computational analysis shows that spectral regression is much faster than the traditional approaches. Our experiments are based on the CASIA and FRGC 3D face databases which contain a huge number of challenging data. Experimental results show that our framework consistently outperforms the other existing methods with the distinctive characteristics of efficiency, robustness and generality

Face recognition using nonparametric-weighted Fisherfaces

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval

Computational Intelligence in Automatic Face Age Estimation: A Survey

With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics, and information retrieval. Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete. Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques. First, we summarize the state-of-the-art databases and the performance metrics for face age estimation. Then, we review the age estimation techniques based on three categories of face features (local, global, and hybrid) and discuss different types of age learning algorithms. Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation

High Performance Techniques for Face Recognition

The identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, makeup, rotations, illuminations, gestures, etc. Also, facial images contain a great deal of redundant information, which negatively affects the performance of the recognition system. The dimensionality and the redundancy of the facial features have a direct effect on the face recognition accuracy. Not all the features in the feature vector space are useful. For example, non-discriminating features in the feature vector space not only degrade the recognition accuracy but also increase the computational complexity. In the field of computer vision, pattern recognition, and image processing, face recognition has become a popular research topic. This is due to its wide spread applications in security and control, which allow the identified individual to access secure areas, personal information, etc. The performance of any recognition system depends on three factors: 1) the storage requirements, 2) the computational complexity, and 3) the recognition rates. Two different recognition system families are presented and developed in this dissertation. Each family consists of several face recognition systems. Each system contains three main steps, namely, preprocessing, feature extraction, and classification. Several preprocessing steps, such as cropping, facial detection, dividing the facial image into sub-images, etc. are applied to the facial images. This reduces the effect of the irrelevant information (background) and improves the system performance. In this dissertation, either a Neural Network (NN) based classifier or Euclidean distance is used for classification purposes. Five widely used databases, namely, ORL, YALE, FERET, FEI, and LFW, each containing different facial variations, such as light condition, rotations, facial expressions, facial details, etc., are used to evaluate the proposed systems. The experimental results of the proposed systems are analyzed using K-folds Cross Validation (CV). In the family-1, Several systems are proposed for face recognition. Each system employs different integrated tools in the feature extraction step. These tools, Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), 2D or 3D DWT, and Fast Independent Component Analysis (FastICA), are applied to the processed facial images to reduce the dimensionality and to obtain discriminating features. Each proposed system produces a unique representation, and achieves less storage requirements and better performance than the existing methods. For further facial compression, there are three face recognition systems in the second family. Each system uses different integrated tools to obtain better facial representation. The integrated tools, Vector Quantization (VQ), Discrete cosine Transform (DCT), and 2D DWT, are applied to the facial images for further facial compression and better facial representation. In the systems using the tools VQ/2D DCT and VQ/ 2D DWT, each pose in the databases is represented by one centroid with 4*4*16 dimensions. In the third system, VQ/ Facial Part Detection (FPD), each person in the databases is represented by four centroids with 4*Centroids (4*4*16) dimensions. The systems in the family-2 are proposed to further reduce the dimensions of the data compared to the systems in the family-1 while attaining comparable results. For example, in family-1, the integrated tools, FastICA/ 2D DMWT, applied to different combinations of sub-images in the FERET database with K-fold=5 (9 different poses used in the training mode), reduce the dimensions of the database by 97.22% and achieve 99% accuracy. In contrast, the integrated tools, VQ/ FPD, in the family-2 reduce the dimensions of the data by 99.31% and achieve 97.98% accuracy. In this example, the integrated tools, VQ/ FPD, accomplished further data compression and less accuracy compared to those reported by FastICA/ 2D DMWT tools. Various experiments and simulations using MATLAB are applied. The experimental results of both families confirm the improvements in the storage requirements, as well as the recognition rates as compared to some recently reported methods

Face Recognition Through Regret Minimization.

Face Recognition is an important problem for Artificial Intelligence Researchers, with applications to law enforcement, medicine and entertainment. Many different approaches to the problem have been suggested most approaches can be categorized as being either Holistic or Local. Recently, local approaches have shown some gains. This thesis presents a system for embedding a holistic algorithm into a local framework. The system proposed builds on the concept of Regional Voting, to create Weighted Regional Voting which divides the face images to be classified into regions, performs classification on each region, and finds the final classification through a weighted majority vote on the regions. Three different weighting schemes taken from the field of Regret Minimization are suggested, and their results compared. Weighted Regional Voting is shown to improve upon unweighted Regional Voting in every case, and to outperform or equal many modern face recognition algorithms. --P. ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b174112