Robust multimodal multivariate ear recognition using kernel based simultaneous sparse representation

基于核联合稀疏表示的强大多模式多元耳朵识别

Engineering Applications of Artificial Intelligence, Volume 64, September 2017, Pages 340-351

Sayan Banerjee, Amitava Chatterjee

Abstract： In this paper, we propose a novel multivariate multimodal ear recognition method which exploits correlation between left and right ear modality of an individual for his/her identification using joint sparse representation and its variant, joint dynamic sparse representation based classification approach. To make the problem much more robust against outliers that might be resulted from illumination variation or noises due to inaccurate measurements or from partial occlusion due to hair or ornaments — especially for female subjects, we employ a novel weighted multivariate regression scheme under joint sparse as well as joint dynamic sparse penalization. That particular scheme learns a set of weights iteratively for each and every residual corresponding to each observation and subsequently, during the time of classification, gives lesser weight to elements detected as outliers such that they are not able to participate for query set representation. To further improve accuracy of the system, the proposed method is kernelized to tackle non-linearity infusion made by pose variations and occlusions. In the end, extensive experimentations are carried out over a novel database developed in our laboratory to compare performance of the proposed method to several competitive, state-of-the-art methods in order to check suitability of the proposed classification method for various real life applications.

Automatic retrieval of shoeprint images using blocked sparse representation

基于分块稀疏表示的鞋印图像自动检索

Forensic Science International, Volume 277, August 2017, Pages 103-114

Sayyad Alizadeh, Cemal Kose

Abstract：Shoe marks are regarded as remarkable clues which can be usually detected in crime scenes where forensic experts use them for investigating crimes and identifying the criminals. Hence, developing a robust method for matching shoeprints with one another is of critical significance which can be used for recognizing criminals. In this paper, a promising method is proposed for retrieving shoe marks by means of developing blocking sparse representation technique. In the proposed method, the queried image was divided into two blocks. Then, two sparse representations are extracted for each queried image through approximate ℓ1 minimizing method. Also, the referenced database is categorized into two parts and two separate dictionaries are developed via them. Next, using the blocks, the total errors of classes are measured by resetting the coefficients related to other classes into zero. The performance of the proposed method was evaluated via the following methods Wright’s sparse representation, extracting shoeprint image local and global features by Fourier transform, extracting shoeprint image features by Gabor transform after the image is rotated and extracting the corners of shoeprint image by Hessian and Harris’ multi-scale detectors and SIFT descriptors. Accurate detection score was obtained in terms of the ratio of the number of accurately detected images to the total test images. The results of simulations indicated that the proposed method was highly effective and efficient in retrieving shoe marks, whole shoeprints, partial toe and heel shoeprints. Furthermore, it was found that the proposed method had better performance than the other methods with which it was compared. Accurate identification rate according to cumulative match score for the first n matches was measured. That is to say, the proposed method accurately recognized 99.47% of whole shoeprints, 80.53% of partial toe shoeprints and 79.47% of partial heel shoeprints in the first rank. Also, the proposed method was compared with the other methods in terms of rotation and scale distortions. The results indicated that the proposed method was resistant to these distortions.

Efficient classification with sparsity augmented collaborative representation

稀疏增强协同表示在有效分类中的应用

Pattern Recognition, Volume 65, May 2017, Pages 136-145

Naveed Akhtar, Faisal Shafait, Ajmal Mian

Abstract：Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It is a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that makes the scheme effective. This claim is attractive as it allows to relinquish the computationally expensive sparsity constraint over the representation. In this paper, we first extend the analysis supporting this claim and then show that sparseness explicitly contributes to improved classification, hence it should not be completely ignored for computational gains. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method that capitalizes on the resulting representation. The augmented representation and the classification method work together meticulously to achieve higher accuracy and lower computational time compared to state-of-the-art collaborative representation based classification approaches. Experiments on benchmark face, object, action and scene databases show the efficacy of our approach.