Department of Computer Science & Engineering

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IIT Jodhpur

Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating overlap in this case, without considering explicit properties of users or items involved, limiting their applicability in domains with very sparse rating spaces. In many domains such as movies, news or electronic commerce recommenders, considerable contextual data in text form describing item properties is available along with the rating data, which could be utilized to improve recommendation quality. In this paper, we propose a novel approach to improve standard CF based recommenders by utilizing latent Dirichlet allocation (LDA) to learn latent properties of items, expressed in terms of topic proportions, derived from their textual description. We infer user's topic preferences or user profile in the same latent space, based on her historical ratings. While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space. This approach alleviates sparsity problem as it allows calculation of similarity between users even if they have not rated any items in common. Our experiments on multiple public datasets indicate that the proposed hybrid approach significantly outperforms standard User Based and Item Based CF recommenders in terms of classification accuracy metrics such as precision, recall and F-measure. © 2014 IEEE.

World University root node

Proceedings - 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2014

Improving collaborative filtering based recommenders using topic modelling

This chapter focuses on recognizing faces with variations in aging and disguise. In the proposed approach, mutual information based age transformation algorithm registers the gallery and probe face images and minimizes the variations in facial features caused due to aging. Further, gallery and probe face images are decomposed at different levels of granularity to extract non-disjoint spatial features. At the first level, face granules are generated by applying Gaussian and Laplacian operators to extract features at different resolutions and image properties. The second level of granularity divides the face image into vertical and horizontal regions of different sizes to specifically handle variations in pose and disguise. At the third level of granularity, the face image is partitioned into small grid structures to extract local features. A neural network architecture based 2D log polar Gabor transform is used to extract binary phase information from each of the face granules. Finally, likelihood ratio test statistics based support vector machine classification approach is used to classify the granular information. The proposed algorithm is evaluated on multiple databases comprising of disguised faces of real people, disguised synthetic face images, faces with aging variations, and disguised faces of actors and actresses from movie clips that also have aging variations. These databases cover a comprehensive set of aging and disguise scenarios. The performance of the proposed algorithm is compared with existing algorithms and the results show that the performance of the proposed algorithm is significantly better than existing algorithms. © 2014 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Electrical Engineering

Recognizing altered facial appearances due to aging and disguise

Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery.A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges. © 1992-2012 IEEE.

IEEE Transactions on Image Processing

Improving cross-resolution face matching using ensemble-based co-transfer learning

With the increase in age, there are changes in skeletal structure, muscle mass, and body fat. For recognizing faces with age variations, researchers have generally focused on the skeletal structure and muscle mass. However, the effect of change in body fat has not been studied with respect to face recognition. In this paper, we incorporate weight information to improve the performance of face recognition with age variations. The proposed algorithm utilizes neural network and random decision forest to encode age variations across different weight categories. The results are reported on the Wholslt database prepared by the authors containing 1109 images from 110 individuals with age and weight variations. The comparison with existing state-of-the-art algorithms and commercial system on Wholslt and FG-Net databases shows that the proposed algorithm outperforms existing algorithms significantly. © 2014 IEEE.

IEEE Access

On recognizing face images with weight and age variations

The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral face database, three HOG variants are analyzed along with dimensionality reduction approaches and linear discriminant analysis. The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15%. We also observe that histogram of oriented gradient features are able to encode similar facial features across spectrums. © 2014 IEEE.

Proceedings - International Conference on Pattern Recognition

On effectiveness of histogram of oriented gradient features for visible to near infrared face matching

The Compressed Sensing (CS) is a new signal acquisition technique that enables the reduction in the number of measurements required for recovery of sparse signal and the signal recovery is done using optimization techniques. The recovery of the original signal from CS measurements becomes difficult when the CS data acquisition is noisy. Here we are conducting a review of robustness of some of these algorithms in recovering the original signal in presence of data acquisition noise. The algorithms are implemented using Matlab and the performance evaluation are done based on average relative error in recovery and the probability of error in signal support. The results of the experimental evaluation using generic sparse data are presented. In general, the relaxation based algorithms are found to have better recovery precision, when the CS measurement is noisy; and the performance of ANN based RASR algorithm is found to be comparable to basis pursuit and IRLS algorithms. © 2014 IEEE.

2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014

Noise immunity analysis of compressed sensing recovery algorithms

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is designed to distinguish humans from machines. Most of the existing tests require reading distorted text embedded in a background image. However, many existing CAPTCHAs are either too difficult for humans due to excessive distortions or are trivial for automated algorithms to solve. These CAPTCHAs also suffer from inherent language as well as alphabet dependencies and are not equally convenient for people of different demographics. Therefore, there is a need to devise other Turing tests which can mitigate these challenges. One such test is matching two faces to establish if they belong to the same individual or not. Utilizing face recognition as the Turing test, we propose FR-CAPTCHA based on finding matching pairs of human faces in an image. We observe that, compared to existing implementations, FR-CAPTCHA achieves a human accuracy of 94% and is robust against automated attacks. © 2014 Goswami et al.

Fulltext

PLoS ONE

FR-CAPTCHA: CAPTCHA based on recognizing human faces

Videos have ample amount of information in the form of frames that can be utilized for feature extraction and matching. However, face images in not all of the frames are 'memorable' and useful. Therefore, utilizing all the frames available in a video for recognition does not necessarily improve the performance but significantly increases the computation time. In this research, we present a memorability based frame selection algorithm that enables automatic selection of memorable frames for facial feature extraction and matching. A deep learning algorithm is then proposed that utilizes a stack of denoising autoencoders and deep Boltzmann machines to perform face recognition using the most memorable frames. The proposed algorithm, termed as MDLFace, is evaluated on two publicly available video face databases, Youtube Faces and Point and Shoot Challenge. The results show that the proposed algorithm achieves state-of-the-art performance at low false accept rates. © 2014 IEEE.

IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics

MDLFace: Memorability augmented deep learning for video face recognition

Humans are very efficient at recognizing familiar face images even in challenging conditions. One reason for such capabilities is the ability to understand social context between individuals. Sometimes the identity of the person in a photo can be inferred based on the identity of other persons in the same photo, when some social context between them is known. This research presents an algorithm to utilize co-occurrence of individuals as the social context to improve face recognition. Association rule mining is utilized to infer multi-level social context among subjects from a large repository of social transactions. The results are demonstrated on the G-album and on the SN-collection pertaining to 4675 identities prepared by the authors from a social networking website. The results show that association rules extracted from social context can be used to augment face recognition and improve the identification performance. © 2014 IEEE.

Aiding face recognition with social context association rule based re-ranking

In this paper we propose a novel character recognition method for Bangla compound characters. Accurate recognition of compound characters is a difficult problem due to their complex shapes. Our strategy is to decompose a compound character into skeletal segments. The compound character is then recognized by extracting the convex shape primitives and using a template matching scheme. The novelty of our approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components. Our technique is applicable to both printed and handwritten characters. The proposed method performs well for complex-shaped compound characters, which were confusing to the existing methods. © 2013 Elsevier Ltd. All rights reserved.