Illumination compensation for face recognition using adaptive singular value decomposition in the wavelet domain

Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition

Many researchers have investigated the time-varying (TV) matrix pseudoinverse problem in recent years, for its importance in addressing TV problems in science and engineering. In this paper, the problem of calculating the inverse or pseudoinverse of an arbitrary TV real matrix is considered and addressed using the singular value decomposition (SVD) and the zeroing neural network (ZNN) approaches. Since SVD is frequently used to compute the inverse or pseudoinverse of a matrix, this research proposes a new ZNN model based on the SVD method as well as the technique of Tikhonov regularization, for solving the problem in continuous time. Numerical experiments, involving the pseudoinversion of square, rectangular, singular, and nonsingular input matrices, indicate that the proposed models are effective for solving the problem of the inversion or pseudoinversion of time varying matrices.

Log-Polar Facial Recognition Using Minimum Feature Band

There is an increasing demand for biometric security systems in several fields. This study presents a highly accurate facial recognition method that uses high-speed transformation and facial morphing using region-limited log-polar transformation based on a center point calculated from the coordinates of both eyes and corners of the mouth. Log-polar transformation is limited to the region, so that the region including the feature can be suppressed to the minimum range, thereby facilitating high-speed transformation. Additionally, after facial morphing, the shapes of the eyes and mouth are altered based on the outline of the face, enabling high-precision facial recognition. The efficacy of the proposed method is verified experimentally. Therefore, we can confirm that 91.72% of images using the color FERET database and 96% of images using the FEI face database can be recognized using our method.

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.

Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.