New Finger Biometric Method Using Near Infrared Imaging

Abstract

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%.

Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

“…The LBP operators 1 are tested with different sizes, the highest CIR obtained for the images of the dataset is the LBP (16,7) (Table 1). Different sub-image sizes were considered, 16 square sub-images results in the best performance ( Table 2).…”

“…A mapping table was created based on the mean and standard deviation of bins, over descriptors from 10 persons. A CIR of 86% is obtained with a descriptor of length 42 extracted by the LBP (16,7) over a sub-image (the image is divided into 16 square sub-images).…”

“…The LBP operator has been proposed for face recognition [2,3], finger vein recognition [8], dorsal hand vein recognition [14,15] and palm-print recognition [10]. The LDP operator has been proposed for face recognition [17] and finger vein recognition [7,6]. However, palm vein recognition problem has not been investigated from such point of view.…”

Singular value decomposition based minutiae matching method for finger vein recognition

“…In ROI-based methods [16][17][18][19], the features are extracted from the whole ROI without finger vein network segmentation. Although promising experimental results are reported in [16][17][18][19]; in practice, these ROI-based methods may suffer from some limitations.…”

Facial Feature Movements Caused by Various Emotions: Differences According to Sex

“…Figure 5 shows the conceptual diagram of the facial feature alignment method. An ordered set of binary values can be expressed in decimal form as shown in Equation (1) [45]:…”