The periocular region is used for authentication in the recent days under unconstrained acquisition in biometrics. This work presents two new feature extraction techniques to achieve robust and blur invariant biometric verification using periocular images captured using smartphones - (1) Deep Sparse Features (DSF) and (2) Deep Sparse Time Frequency Features (DeSTiFF). Both the approaches are based on extracting features via convolution of periocular images with a set of filters also referred as Deep Sparse Filters. The filters are learnt using natural image patches and sparse filtering approach. The DSF is obtained through convolution via Deep Sparse Filters. Further, convoluted responses are analyzed using Short Term Fourier Transform (STFT) to obtain time and frequency features of the images referred as DeSTIFF. The features obtained from the newly proposed feature extraction techniques are further represented in a collaborative subspace to achieve better verification performance. Both of the proposed feature extraction schemes are evaluated on two publicly available smartphone periocular databases and a new database (Visible Spectrum Periocular Image (VISPI) database) released with this article. The robustness of the proposed feature extraction is exemplified by comparing it with state-of-art approaches along with multiple deep networks where the improvement is evidently seen on large scale database with an average verification accuracy of Genuine Match Rate ≈ 98% at False Match Rate = 0.01%. We further support reproducible research by making the code and the database available for the academic research.

在生物识别技术的无限制获取下，近眼周区域用于鉴定。这项工作提出了两种新的特征提取技术，以使用通过智能手机捕获的眼周图像实现鲁棒且模糊的不变生物特征验证-（1）深度稀疏特征（DSF）和（2）深度稀疏时频特征（DeSTiFF）。两种方法都基于通过使用一组滤镜（也称为深稀疏滤镜）对眼周图像进行卷积来提取特征的方法。使用自然图像补丁和稀疏过滤方法来学习过滤器。通过深稀疏滤波器通过卷积获得DSF。此外，使用短期傅立叶变换（STFT）分析卷积响应，以获得称为DeSTIFF的图像的时间和频率特征。从新提出的特征提取技术获得的特征将在协作子空间中进一步表示，以实现更好的验证性能。两种提议的特征提取方案都在两个公开可用的智能手机眼周数据库和本文发布的新数据库（可见光谱眼周图像（VISPI）数据库）上进行了评估。通过将其与最新方法以及多个深度网络进行比较，可以证明所提出的特征提取的鲁棒性，在大型数据库中显然可以看到这种改进，平均验证准确度为真正匹配率≈98％（错误匹配率= 0.01％）。通过使代码和数据库可用于学术研究，我们进一步支持可重复的研究。