Nighttime Periocular Recognition at Long Standoffs with Deep Learned Features

The periocular region is considered as a relatively new modality of biometrics and serves as a substitute solution for face recognition with occlusion. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance. To address this problem, we study the topic of periocular recognition at nighttime using the infrared spectrum. Utilizing a simplified version of DeepFace, a convolutional neural networks designed for face recognition, we investigate nighttime periocular recognition at both short and long standoffs, namely 1.5 m, 50 m and 106 m. A subband of the active infrared spectrum - near-infrared (NIR) - is involved. During generation of the periocular dataset, preprocessing is conducted on the original face images, including alignment, cropping and intensity conversion. The verification results of the periocular region using DeepFace are compared with the results of two conventional methods - LBP and PCA. Experiments have shown that the DeepFace algorithm performs fairly well (with GAR over 90% at FAR=0.1%) using the periocular region as a modality even at nighttime. The framework also shows superiority to both LBP and PCA in all cases of different light wavelengths and standoffs.