Richa Singh

Bio: Richa Singh is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Facial recognition system & Deep learning. The author has an hindex of 53, co-authored 422 publications receiving 9145 citations. Previous affiliations of Richa Singh include Indian Institutes of Technology & University of Virginia.

Unraveling the Effect of Textured Contact Lenses on Iris Recognition

Abstract: The presence of a contact lens, particularly a textured cosmetic lens, poses a challenge to iris recognition as it obfuscates the natural iris patterns. The main contribution of this paper is to present an in-depth analysis of the effect of contact lenses on iris recognition. Two databases, namely, the IIIT-D Iris Contact Lens database and the ND-Contact Lens database, are prepared to analyze the variations caused due to contact lenses. We also present a novel lens detection algorithm that can be used to reduce the effect of contact lenses. The proposed approach outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance.

Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning

Abstract: In movies, film stars portray another identity or obfuscate their identity with the help of silicone/latex masks. Such realistic masks are now easily available and are used for entertainment purposes. However, their usage in criminal activities to deceive law enforcement and automatic face recognition systems is also plausible. Therefore, it is important to guard biometrics systems against such realistic presentation attacks. This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario. Along with silicone mask, there are several other presentation attack instruments that are explored in literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation attack detection algorithm that can discern different kinds of attacks. An efficient greedy layer by layer training approach is formulated to learn the deep dictionaries followed by SVM to classify an input sample as genuine or attacked. Experimental are performed on the proposed SMAD database, some samples with real world silicone mask attacks, and four existing presentation attack databases, namely, replay-attack, CASIA-FASD, 3DMAD, and UVAD. The results show that the proposed algorithm yields better performance compared with state-of-the-art algorithms, in both intra-database and cross-database experiments.

Deep dictionary learning

Abstract: Two popular representation learning paradigms are dictionary learning and deep learning. While dictionary learning focuses on learning “basis” and “features” by matrix factorization, deep learning focuses on extracting features via learning “weights” or “filter” in a greedy layer by layer fashion. This paper focuses on combining the concepts of these two paradigms by proposing deep dictionary learning and show how deeper architectures can be built using the layers of dictionary learning. The proposed technique is compared with other deep learning approaches, such as stacked autoencoder, deep belief network, and convolutional neural network. Experiments on benchmark data sets show that the proposed technique achieves higher classification and clustering accuracies. On a real-world problem of electrical appliance classification, we show that deep dictionary learning excels where others do not yield at-par performance. We postulate that the proposed formulation can pave the path for a new class of deep learning tools.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …