Department of Computer Science & Engineering

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IIT Jodhpur

Automated facial expression classification has widespread application in multiple domains such as human computer interaction, health and entertainment, biometrics, and security. There are six basic facial expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise, apart from a neutral state. Most of the research in expression classification has focused on adult face images, with no dedicated research on automating expression classification for children. To the best of our knowledge, this is the first research which presents a deep learning based expression classification approach for children. A novel supervised deep learning formulation, termed as Mean Supervised Deep Boltzmann Machine (msDBM) is proposed which classifies an input face image into one of the seven expression classes. The proposed approach has been evaluated on two child face datasets - Radboud Faces and CAFE, along with experiments on the adult face images of the Radboud Faces dataset. Experimental results and analysis reinforces the challenging nature of the task at hand, and the effectiveness of the proposed msDBM model. © 2019 IEEE.

World University root node

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Expression classification in children using mean supervised deep boltzmann machine

Large-scale identity projects such as the Unique Identification Authority of India (UIDAI) comprise of multiple individual organizations, which may use different sensors for enrolling the individuals while the data obtained at the time of verification can be collected from a different sensor. In such multi-camera scenario, it is imperative to perform image-based iris sensor identification. In this research, we propose an efficient algorithm to identify the sensor from which the iris image is captured. The proposed algorithm is the amalgamation of SVM fitness function based Bacteria Foraging (BF) feature selection and fusion of multiple features such as Block Image Statistical Measure (BISM), High Order Wavelet Entropy (HOWE), Texture Measure (TM), Single-level Multi-orientation Wavelet Texture (SlMoWT), and Image Quality Measures (IQM). The selected features are then given input to a supervised classification algorithm for iris sensor identification. The second contribution of this research is developing two sets of multisensor iris image databases that, in total, contain 6000 images with over 150 subjects. The results show that the proposed sensor classification algorithm is computationally very fast and yields an accuracy of over 99% on multiple databases. © 2018 Elsevier B.V.

Information Fusion

Iris sensor identification in multi-camera environment

Background: Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach. New Method: We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity. Results: Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment. Comparison: Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation. Conclusion: The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications. © 2019 Elsevier B.V.

School of Artificial Intelligence and Data Science (AIDE)

Journal of Neuroscience Methods

Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach

A graph is called a split graph if its vertex set can be partitioned into a clique and an independent set. Split graphs have rich mathematical structure and interesting algorithmic properties making it one of the most well-studied special graph classes. In the Split Vertex Deletion(SVD) problem, given a graph and a positive integer k, the objective is to test whether there exists a subset of at most k vertices whose deletion results in a split graph. In this paper, we design a kernel for this problem with vertices, improving upon the previous cubic bound known. Also, by giving a simple reduction from the Vertex Cover problem, we establish that SVD does not admit a kernel with edges, for any, unless. © 2019, Springer Nature Switzerland AG.

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

Quadratic Vertex Kernel for Split Vertex Deletion

Existing face recognition algorithms achieve high recognition performance for frontal face images with good illumination and close proximity to the imaging device. However, most of the existing algorithms fail to perform equally well in surveillance scenarios, where videos are captured across varying resolutions and spectra. In surveillance settings, cameras are usually placed far away from the subjects, thereby resulting in variations across pose, illumination, occlusion, and resolution. Current video datasets used for face recognition are often captured in constrained environments, and thus fail to simulate the real world scenarios. In this paper, we present the FaceSurv database featuring 252 subjects in 460 videos. The proposed dataset contains over 142K face images, spread across videos captured in both visible and near-infrared spectra. Each video contains a group of individuals walking from 36ft towards the imaging device, offering a plethora of challenges common to surveillance settings. Benchmark experimental protocol and baseline results have been reported with state-of-the-art algorithms for face detection and recognition. It is our assertion that the availability of such a challenging database will facilitate the development of robust face recognition systems relevant to real. © 2019 IEEE.

Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

FaceSurv: A Benchmark video dataset for face detection and recognition across spectra and resolutions

Generative models are becoming increasingly popular in the literature, with Generative Adversarial Networks (GAN) being the most successful variant, yet. With this increasing demand and popularity, it is becoming equally difficult and challenging to implement and consume GAN models. A qualitative user survey conducted across 47 practitioners show that expert level skill is required to use GAN model for a given task, despite the presence of various open source libraries. In this research, we propose a novel system called AuthorGAN, aiming to achieve true democratization of GAN authoring. A highly modularized library agnostic representation of GAN model is defined to enable interoperability of GAN architecture across different libraries such as Keras, Tensorflow and PyTorch. An intuitive drag-and-drop based visual designer is built using node-red platform to enable custom architecture designing without the need for writing any code. Five different GAN models are implemented as a part of this framework and the performance of the different GAN models are shown using the benchmark MNIST dataset.

arXiv preprint arXiv:1911.

AuthorGAN: Improving GAN Reproducibility using a Modular GAN Framework

US Patent App. 15/882,087

Mitigating false recognition of altered inputs in convolutional neural networks

IIITD-Delhi

Self-supervised and collaborative learning

Universal face detection

In this paper we propose a distributed locality sensitive hashing based framework for image super resolution exploiting computational and storage efficiency of cloud. Now days huge multimedia data is available on the cloud which can be utilized using store anywhere and excess anywhere model. It may be noted that super resolution is required for consumer electronics display devices due to various reasons. The propose framework exploits the image correlation for image super resolution using locality sensitive hashing (LSH) for manifold learning. In our work we have exploited the benefits of manifold learning for image super resolution, which in-turn is a highly time complex operation. The time complexity is involved due to finding the approximate nearest neighbors from trillion of image patches for locally linear embedding (LLE) operation. In our approach it is mitigated by using a distributed framework which internally uses hash tables for mapping of patches in the target image from a database of internet picture collection. The proposed framework for super resolution provides promising results in comparison to existing approaches. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.