Abstract A fingerprint‐based authentication system provides security to the applications of numerous fields and usually stores minutiae information in the database as a template. It has been observed from the literature that the reconstruction of an original fingerprint is possible from minutiae points information; hence the security of the stored template becomes extremely crucial. Cancellable biometric techniques based on non‐invertible transformation protect the stored template. These techniques prevent the reconstruction of original fingerprint data from the compromised template and avoid unauthorized access to the system. In this paper, a technique based on the non‐invertible transformation to protect a fingerprint template is proposed. In the technique, minutiae locations in a fingerprint are transformed by using the minutiae's original locations and orientation information, and a user keyset. A principal component analysis based approach to align the probe and gallery templates of fingerprint images while matching is also proposed. The evaluation of the proposed technique is carried out on seven different fingerprint databases taken from FVC2000, FVC2002, and FVC2004 databases, and its performance is compared with other existing state‐of‐the‐art techniques in the literature. The comparative performance shows that the proposed technique is highly robust and performs exceptionally well compared to other existing techniques.

The unalterable nature of fingerprint biometrics leads to permanent identity loss of a user if an original fingerprint template is compromised. Moreover, it is evident in the literature that reconstructing a fingerprint image from an original fingerprint template is a feasible task. In order to protect the fingerprint template, we propose a noninvertible and alignment-free fingerprint template protection technique by exploiting the pair-polar minutiae structures. In the proposed technique, a secure fingerprint template is generated in the form of a set of binary vectors. These vectors are computed from the many-to-one mapping of the transformed pair-polar coordinates into a 3-D grid. Results of the proposed technique are analyzed in terms of revocability, unlinkability, security, and performance on six publicly available databases of Fingerprint Verification Competition 2002 and 2004. The overall analysis of the results clearly shows the effectiveness of the proposed technique as compared to the existing state-of-the-art techniques.

Due to the availability of cheap 3D sensors such as Kinect and LiDAR, the use of 3D data in various domains such as manufacturing, healthcare, and retail to achieve operational safety, improved outcomes, and enhanced customer experience has gained momentum in recent years. In many of these domains, object recognition is being performed using 3D data against the difficulties posed by illumination, pose variation, scaling, etc present in 2D data. In this work, we propose three data augmentation techniques for 3D data in point cloud representation that use sub-sampling. We then verify that the 3D samples created through data augmentation carry the same information by comparing the Iterative Closest Point Registration Error within the sub-samples, between the sub-samples and their parent sample, between the sub-samples with different parents and the same subject, and finally, between the sub-samples of different subjects. We also verify that the augmented sub-samples have the same characteristics and features as those of the original 3D point cloud by applying the Central Limit Theorem.

Summary This paper presents a technique for ear detection from 2D profile face images that is capable of significantly reducing the false positives. In an ear biometrics system, recognition performance highly depends on the performance of the ear detection module. The trade‐off between the complexity and the false positive detection is one of the essential component, where the complexity of a system increases proportionally to achieve a zero false positive rate detection. In literature, available ear detection techniques based on handcrafted features face challenges with low‐quality acquired images affected by illumination, occlusion, and pose variations. We propose an ear detection technique using ensemble of convolutional neural network (CNN). The first part of the technique trains three models of CNN on a given dataset, whereas in later part, weighted average of the outputs of trained models is utilized to detect the ear regions. The used ensemble models show better performance as compared to the case when each individual model is used standalone. The proposed technique is being evaluated on two databases, viz, IIT Indore‐Collection A (IIT‐Col A) database and annotated web ear (AWE) database. Experimental results of ear detection demonstrate the superior performance of the proposed technique over other state‐of‐the‐art techniques in handling illumination, occlusion, and pose variations.

This study presents an efficient no‐reference image quality assessment (NR‐IQA) technique to assess the quality of images affected by noise. The proposed technique is based on two characteristics of the human eye (retina), namely the presence of centre‐surround receptive field and visualisation utilising different spatial frequency channels. In the proposed technique, the authors model centre‐surround receptive field using difference of Gaussians (DoG), whereas to mimic multiple frequencies in the centre‐surround receptive field, they compute multiple DoG images of different values of standard deviations generated for different frequencies. Furthermore, the singular value decomposition‐based features are obtained from the generated DoG images to estimate the image quality. The proposed technique does not require any training, neither based on distorted/original images nor based on subjective human scores, to assess the image quality. The performance of the proposed technique is being analysed on LIVE, TID08, CSIQ and SD‐IVL databases and it shows that the proposed technique outperforms recently proposed NR and no‐training/training‐based IQA techniques. Experimental validation of the proposed technique in the big‐data scenario of 10,000 noisy images also shows encouraging results.

This paper presents an enhanced geometric hashing technique suitable for object recognition. Unlike the available geometric hashing, the proposed technique needs less amount of time and memory, uniform index distribution in the hash space without using any rehashing function. It performs indexing and searching in one pass with linear complexity. The proposed technique has been applied in biometric databases. It has been tested for three traits such as ear, iris and palm print. The hitrate of 100% has been achieved for top 5 best matches in all cases.

This paper presents an efficient technique for automatic ear detection from side face images. The proposed technique detects ear by exploiting its inherent structural details and is rotation, scale and shape invariant. It can detect ear without any training or assuming prior knowledge of the input image. The technique is based on connected component analysis of a graph constructed using the edge map of the image and is evaluated on a data set consisting of 2361 side face images collected at IIT Kanpur. Ear detection results are found to be very good and speak for the efficiency and robustness of the technique. To show the accuracy of the detection, detected ears are used for recognition and results are compared with the same obtained when ear cropping in done manually.