Automated personal authentication has become increasingly important in modern information driven society and in this regard fingerprint‐based personal identification is considered to be the most effective tool. In order to ensure reliable fingerprint identification and improve fingerprint ridge structure, a novel fingerprint enhancement approach is presented based on local adaptive contextual filtering. The proposed enhancement technique is 2‐fold as it involves processing both in frequency and spatial domain. The fingerprint image is first filtered in frequency domain and then local directional filtering in spatial domain is applied to obtain enhanced fingerprint. In order to determine the performance efficiency of the proposed enhancement technique, a comparative analysis of error rates on standard fingerprint databases has been presented with major contextual enhancement schemes. The results show the efficacy of the proposed scheme as compared with other contextual filtering techniques.

Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentation, and detection. In this paper, we propose a two-staged glaucoma classification scheme based on Deep CNN architectures. In stage one, four different ImageNet pre-trained Deep CNN architectures, i.e., AlexNet, InceptionV3, InceptionResNetV2, and NasNet-Large are used and it is observed that NasNet-Large architecture provides superior performance in terms of sensitivity (99.1%), specificity (99.4%), accuracy (99.3%), and area under the receiver operating characteristic curve (97.8%) metrics. A detailed performance comparison is also presented among these on public datasets, i.e., ACRIMA, ORIGA-Light, and RIM-ONE as well as locally available datasets, i.e., AFIO, and HMC. In the second stage, we propose an ensemble classifier with two novel ensembling techniques, i.e., accuracy based weighted voting, and accuracy/score based weighted averaging to further improve the glaucoma classification results. It is shown that ensemble with accuracy/score based scheme improves the accuracy (99.5%) for diverse databases. As an outcome of this study, it is presented that the NasNet-Large architecture is a feasible option in terms of its performance as a single classifier while ensemble classifier further improves the generalized performance for automatic glaucoma classification.

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L₂ norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi‐View Average Half Face (d‐MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d‐MVAHF‐Support Vector Machine (d‐MVAHF‐SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB‐DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state‐of‐the‐art methods.