So Ra Cho

Human Age Estimation Based on Multi-level Local Binary Pattern and Regression Method

In this paper, a novel method for human age estimation is proposed. This research is novel in the following four ways. First, the in-plane rotation of face region is compensated based on the detected positions of two eyes by Adaboost method. The region of interest (ROI) for extracting age features in the detected face region is re-defined based on the distance between two eyes. Second, multi-level local binary pattern (MLBP) method is applied in order to extract the features for age estimation. Third, in order to solve the problem of age estimation by active appearance model (AAM), we extract whole texture information by MLBP which takes low processing time. Fourth, the human age is estimated using support vector regression based on the texture features. The experimental results show that the proposed method can estimate the human age with the mean absolute error (MAE) of 6.58 years.

Human Age Estimation Method Robust to Camera Sensor and/or Face Movement

Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method.

Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation.

Enhancement of Finger-Vein Image by Vein Line Tracking and Adaptive Gabor Filtering for Finger-Vein Recognition

Biometrics is the technology to identify a user by using the physiological or behavioral characteristics. Among the biometrics such as fingerprint, face, iris, and speaker recognition, finger-vein recognition has been widely used in various applications such as door access control, financial security, and user authentication of personal computer, due to its advantages such as small sized and low cost device, and difficulty of making fake vein image. Generally, a finger-vein system uses near-infrared (NIR) light illuminator and camera to acquire finger-vein images. However, it is difficult to obtain distinctive and clear finger-vein image due to skin scattering of illumination since the finger-vein exists inside of a finger. To solve these problems, we propose a new method of enhancing the quality of finger-vein image. This research is novel in the following three ways compared to previous works. First, the finger-vein lines of an input image are discriminated from the skin area by using local binarization, morphological operation, thinning and line tracing. Second, the direction of vein line is estimated based on the discriminated finger-vein line. And the thickness of finger-vein in an image is also estimated by considering both the discriminated finger-vein line and the corresponding position of finger-vein region in an original image. Third, the distinctiveness of finger-vein region in the original image is enhanced by applying an adaptive Gabor filter optimized to the measured direction and thickness of finger-vein area. Experimental results showed that the distinctiveness and consequent quality of finger-vein image are enhanced compared to that without the proposed method.

Periocular-based biometrics robust to eye rotation based on polar coordinates

Conventional iris recognition requires a high-resolution camera equipped with a zoom lens and a near-infrared illuminator to observe iris patterns. Moreover, with a zoom lens, the viewing angle is small, restricting the user’s head movement. To address these limitations, periocular recognition has recently been studied as biometrics. Because the larger surrounding area of the eye is used instead of iris region, the camera having the high-resolution sensor and zoom lens is not necessary for the periocular recognition. In addition, the image of user’s eye can be captured by using the camera having wide viewing angle, which reduces the constraints to the head movement of user’s head during the image acquisition. Previous periocular recognition methods extract features in Cartesian coordinates sensitive to the rotation (roll) of the eye region caused by in-plane rotation of the head, degrading the matching accuracy. Thus, we propose a novel periocular recognition method that is robust to eye rotation (roll) based on polar coordinates. Experimental results with open database of CASIA-Iris-Distance database (CASIA-IrisV4) show that the proposed method outperformed the others.

Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP

Because of its many useful applications, human age estimation has been considered in many previous studies as a soft biometrics. However, most existing methods of age estimation require a clear and focused facial image as input in order to obtain a trustworthy estimation result; otherwise, the methods might produce increased estimation error when an image of poor quality is used as input. Image blurring is one of major factors that affect estimation accuracies because it can cause a face to appear younger (i.e., reduce the age feature in the face region). Therefore, we propose a new human age estimation method that is robust even with an image that has the optical blurring effect by using symmetrical focus mask and sub-blocks for multi-level local binary pattern (MLBP). Experiment results show that the proposed method can enhance age estimation accuracy compared with the conventional system, which does not consider the effects of blurring.

Periocular Recognition Based on LBP Method and Matching by Bit-Shifting

Periocular recognition requires neither a high-resolution camera nor a zoom lens. It matches using the features extracted from the surrounding area of the eye. In addition, by using a wide-view camera, the constraints to users’ head movement decrease. In this research, we newly propose a periocular recognition based on LBP method and matching by bit-shifting. Our research is novel in the following three manners. First, the iris and pupil region in the input eye image are detected. This allows the accurate eye region to be obtained for periocular recognition. Second, the feature code is extracted from the eye region with a local binary pattern method. Third, the proposed system performs matching by bit-shifting to prevent degradation to the matching accuracy caused by head movement. Experimental results show that the high accuracy of periocular recognition is obtained by the proposed method.

Face Recognition Algorithm for Photographs and Viewed Sketch Matching Using Score-Level Fusion

For criminal searches, the necessity of matching photographs with sketches is increasing. Previously, matching was performed manually by a human observer, a time-consuming process whose accuracy can be affected by the level of human expertise. Therefore, we propose a new face recognition algorithm for photographs and sketches. This research is novel in the following three ways. First, to overcome the decrease in matching accuracy due to pose and illumination variation, we use eye alignment and retinex filtering to normalize pose, size and illumination. Second, we compare the performance of various face recognition methods, such as principal component analysis (PCA), local binary pattern (LBP), local non-negative matrix factorization (LNMF), support vector machine-discriminant analysis (SVM-DA) and modified census transform (MCT), for the matching of photographs and viewed sketches. Third, these five face recognition methods are combined on the basis of score-level fusion to enhance matching accuracy, ther...