Department of Computer Science & Engineering

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IIT Jodhpur

This paper describes a sparse representation based approach to learn a classifier for assessing the video quality without a reference. First we calculate the natural scene statistics (NSS) based spatial features of each frame/image and then learn a dictionary by K-SVD algorithm from NSS features of correct frames. In this work we identified the fact that correct frame can be represented precisely in terms of dictionary atoms but while representing a distorted frame, the error drastically increases with increase in distortion thus we can easily classify the frames as correct and distorted based on error score calculated by sparse representation framework. This framework has been validated on two datasets and we observe improved accuracies as compared to state-of-art algorithms. © 2015 IEEE.

World University root node

2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015

Sparse representation based classifier to assess video quality

Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively. © 2016 IEEE.

Proceedings - International Conference on Pattern Recognition

Face anti-spoofing with multifeature videolet aggregation

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. © 2016 IEEE.

Fulltext

IEEE Access

Deep Dictionary Learning

We discuss the signal tracking approach based phase estimation in digital holographic interferometry and its potential application for continuous evaluation of out-offplane thermal expansion of a metallic surface as a case study. © OSA 2016.

Optics InfoBase Conference Papers

Signal tracking approach based phase estimation for analysis of thermal expansion by digital holographic interferometry

Speech compression or speech coding is inevitable for effective communication of speech signals in resource limited scenarios and researcher’s have been working on achieving lower and lower transmission bit rates (BR) without much compromise on the quality of speech. Medium BR hybrid speech coding schemes have gained much interest in the recent years with most of them based on CELP, the basic medium bit-rate coding scheme. In this work, we provide an insight to the capabilities of compressive sensing (CS) in speech processing and propose a novel idea in the quantized framework. Three major aspects demonstrated in this paper are (1) Inherent de-noising of noisy speech by the CS based coder along with compression (2) Quantization of CS measurements to achieve medium transmission bit-rates and (3) Enhancement of quality and compression performance of the coder with better sparse representations of speech using dictionaries. The results indicate that the proposed scheme offers better compression in comparison with basic Gaussian codebook CELP. The CS scheme has the added advantage of inherent noise suppression and provides more robustness to background noise in comparison with parameter extraction based medium bit-rate speech coding systems. © 2016, Springer Science+Business Media New York.

International Journal of Speech Technology

Simultaneous speech coding and de-noising in a dictionary based quantized CS framework

The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http: //votchallenge.net). © Springer International Publishing Switzerland 2016.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

The visual object tracking VOT2016 challenge results

We discuss the signal tracking approach based phase estimation in digital holographic interferometry and its potential application for continuous evaluation of out-off plane thermal expansion of a metallic surface as a case study. © OSA 2016.

We propose a wrapped statistics-based approach for phase estimation from noisy reconstructed interference fringes in digital holographic interferometry. The state space model required here is formed by Taylor series expansion of the phase function as state model and the wrapped dynamical system as measurement model. Prediction of the state using Kalman filter is straightforward since the state model is linear. However, the non-linearity issue induced due to the wrapping of the measurements is handled by changing the innovation correction step, which accounts for the probability of wrappings. Through the simulation and experimental study, we have shown that the proposed approach is robust to both, noise in fringe pattern as well as the dynamic range of the phase pattern, simultaneously. Moreover, it outperforms when compared with the other state-of-the-art phase retrieval approaches. © 2016 Taylor & Francis.

Journal of Modern Optics

Wrapped statistics-based phase retrieval from interference fringes

Functional Magnetic Resonance Imaging (fMRI) has opened ways to look inside active human brain. However, fMRI signal is an indirect indicator of underlying neuronal activity and has lowtemporal resolution due to acquisition process. This paper proposes autoregressive hidden Markov model with missing data (AR-HMM-md) framework which aims at addressing aforementioned issues while allowing accurate capturing of fMRI time series characteristics. The proposed work models unobserved neuronal activity over time as sequence of discrete hidden states, and shows how exact inference can be obtained with missing fMRI data under the "Missing not at Random" (MNAR) mechanism. This mechanism requires explicit modelling of the missing data along with the observed data. The performance is evaluated by observing convergence characteristic of loglikelihoods and classification capability of the proposed model over existing models for two fMRI datasets. The classification is performed between real fMRI time series from a task-based experiment and randomly-generated time series. Another classification experiment is performed between children and elder subjects using fMRI time series from resting-state data. The proposed model captured the fMRI characteristics efficiently and thus converged to better posterior probability resulting into higher classification accuracy over existing models for both the datasets. © 2016 ACM.

School of Artificial Intelligence and Data Science (AIDE)

ACM International Conference Proceeding Series

Autoregressive hidden Markov model with missing data for modelling functional MR imaging data

Latent Dirichlet Allocation is a widely used approach for topic modeling and it has been successfully applied in several information retrieval applications. In this paper, we introduce this modeling technique for face recognition, by making an analogy between the two domains. We utilize latent Dirichlet allocation to represent facial regions in terms of FaceTopics. Further, linear discriminant analysis is utilized to obtain discriminative FaceTopics which are more suitable for classification tasks. The performance of the proposed approach is evaluated on the CMU-MultiPIE dataset under illumination and expression variations. The evaluation on over more than 50k images shows the effectiveness of the proposed approach. Further, the proposed approach shows improved identification results on e-PRIP dataset for matching composite sketches to photos. © 2016 IEEE.