Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

Abstract

The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network. INDEX TERMS Convolutional neural networks, deep learning, mid-level features, periocular biometrics, periocular recognition.

Deep Learning for Iris Recognition: A Survey

“…In the visual surveillance context, Kim et al [88] infer subjects identities based either in loose/tight regions-of-interest, depending of the perceived image quality. Hwang and Lee [77]prevents the loss of mid-level features and dynamically selects the most important features for classification. Luo et al [102] used self-attention channel and spatial mechanisms into the feature encoding module of a CNN, in order to obtain the most discriminative features of the iris and periocular regions.…”

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

“…Multi-resolution analysis methods, such as wavelet-based [14], [15], and scale-space [16], have been employed to evaluate periocular regions. The periocular recognition system has also been tested using feature descriptor methods such as scale-invariant feature transform (SIFT), speeded up robust features (SURF), binary robust invariant scalable keypoints (BRISK), oriented FAST, and rotated BRIEF (ORB) [17], [18], deep learning and neural network-based [19], [20].…”