Rate-energy-accuracy optimization of convolutional architectures for face recognition

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their adoption on distributed battery-operated devices (e.g., visual sensor nodes, smartphones, and wearable devices). First, convolutional architectures are usually computationally demanding, especially when the depth of the network is increased to maximize accuracy. Second, transmitting the output features produced by a CNN might require a bitrate higher than the one needed for coding the input image. Therefore, in this paper we address the problem of optimizing the energy-rate-accuracy characteristics of a convolutional architecture for face recognition. We carefully profile a CNN implementation on a Raspberry Pi device and optimize the structure of the neural network, achieving a 17-fold speedup without significantly affecting recognition accuracy. Moreover, we propose a coding architecture custom-tailored to features extracted by such model. (C) 2015 Elsevier Inc. All rights reserved.Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their a36142148CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)sem informação2013/11359-0sem informaçã

Recod @ Mediaeval 2014: Diverse Social Images Retrieval

This paper presents the results of the rst participation of our multi-institutional team in the Retrieving Diverse Social Images Task at MediaEval 2014. In this task we were required to develop a summarization and diversi cation approach for social photo retrieval. Our approach is based on irrelevant image ltering, image re-ranking, and diversity promotion by clustering. We have used visual and textual features, including image metadata and user credibility information.1263Carbonell, J., Goldstein, J., The use of mmr, diversity-based reranking for reordering documents and producing summaries (1998) SIGIR, pp. 335-336Ionescu, B., Popescu, A., Lupu, M., Gînscâ, A.L., MüLler, H., Retrieving diverse social images at mediaeval 2014: Challenge, dataset and evaluation (2014) MediaEval 2014 Workshop, , BarcelonaPenatti, O.A.B., Valle, E., Da Torres, R.S., Comparative study of global color and texture descriptors for web image retrieval (2012) J. Vis. Commun. Image Repr., 23 (2), pp. 359-38

Multimedia geocoding: the RECOD 2014 approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This work describes the approach proposed by the RECOD team for the Placing Task of MediaEval 2014. This task requires the definition of automatic schemes to assign geographical locations to images and videos. Our approach is based on the use of as much evidences as possible (textual, visual, and/or audio descriptors) to geocode a given image/video. We estimate the location of test items by clustering the geographic coordinates of top-ranked items in one or more ranked lists defined in terms of different criteria.This work describes the approach proposed by the RECOD team for the Placing Task of MediaEval 2014. This task requires the definition of automatic schemes to assign geographical locations to images and videos. Our approach is based on the use of as much e1263FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2013/08645-0 ; 2013/11359-0306580/2012-8 ; 484254/2012-0sem informaçãoMediaEval 2014 Worksho

The 2013 face recognition evaluation in mobile environment

Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. © 2013 IEEE

InterFace : A software package for face image warping, averaging, and principal components analysis

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the “face space” produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment

Census histograms: A simple feature extraction and matching approach for face recognition

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance

Vehicle License Plate Recognition With Random Convolutional Networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Despite decades of research on automatic license plate recognition (ALPR), optical character recognition (OCR) still leaves room for improvement in this context, given that a single OCR miss is enough to miss the entire plate. We propose an OCR approach based on convolutional neural networks (CNNs) for feature extraction. The architecture of our CNN is chosen from thousands of random possibilities and its filter weights are set at random and normalized to zero mean and unit norm. By training linear support vector machines (SVMs) on the resulting CNN features, we can achieve recognition rates of over 98% for digits and 96% for letters, something that neither SVMs operating on image pixels nor CNNs trained via back-propagation can achieve. The results are obtained in a dataset that has 182 samples per digit and 28 per letter, and suggest the use of random CNNs as a promising alternative approach to ALPR systems.298303CAPES,Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq),Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Du, S., Ibrahim, M., Shehata, M.S., Badawy, W.M., Automatic license plate recognition (ALPR): A state-of-the-art review (2013) IEEE Transactions on Circuits and Systems for Video Technologie, 23 (2), pp. 311-325Anagnostopoulos, C.-N., Anagnostopoulos, I., Psoroulas, I., Loumos, V., Kayafas, E., License plate recognition from still images and video sequences: A survey (2008) IEEE Transactions on Intelligent Transportation Systems, 9 (3), pp. 377-391Hegt, H., De La Haye, R., Khan, N., A high performance license plate recognition system (1998) IEEE International Conference on Systems, Man, and Cybernetics (SMC, 5, pp. 4357-4362Chang, S.-L., Chen, L.-S., Chung, Y.-C., Chen, S.-W., Automatic license plate recognition (2004) IEEE Transactions on Intelligent Transportation Systems, 5 (1), pp. 42-53Lee, E.R., Kim, P.K., Kim, H.J., Automatic recognition of a car license plate using color image processing (1994) IEEE International Conference Image Processing (ICIP, 2, pp. 301-305Matas, J., Zimmermann, K., Unconstrained licence plate and text localization and recognition (2005) IEEE Intelligent Transportation Systems (ITS, pp. 225-230Anagnostopoulos, C.-N., Anagnostopoulos, I., Loumos, V., Kayafas, E., A license plate-recognition algorithm for intelligent transportation system applications (2006) IEEE Transactions on Intelligent Transportation Systems, 7 (3), pp. 377-392Nukano, T., Fukumi, M., Khalid, M., Vehicle license plate character recognition by neural networks (2004) International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS, pp. 771-775Jiao, J., Ye, Q., Huang, Q., A configurable method for multistyle license plate recognition (2009) Pattern Recognition, 42 (3), pp. 358-369Thome, N., Robinault, L., A cognitive and video-based approach for multinational license plate recognition (2011) Machine Vision Applications, 22 (2), pp. 389-407Le Cun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324Cortes, C., Vapnik, V.N., Support-vector networks (1995) Machine Learning, 20 (3), pp. 273-297Wang, T., Wu, D.J., Coates, A., Ng, A.Y., End-to-end text recognition with convolutional neural networks (2012) IEEE International Conference on Pattern Recognition (ICPR, pp. 3304-3308Coates, A., Carpenter, B., Case, S.S.C., Suresh, B., Wu, D.J., Ng, A.Y., Text detection and character recognition in scene images with unsupervised feature learning (2011) IEEE International Conference on Document Analysis and Recognition (ICDAR, pp. 440-445Pinto, N., Cox, D.D., Beyond simple features: A large-scale feature search approach to unconstrained face recognition (2011) IEEE International Conference on Automatic Face and Gesture Recognition (FG, pp. 8-15Pinto, N., Stone, Z., Zickler, T., Cox, D.D., Scaling-up biologicallyinspired computer vision: A case study in unconstrained face recognition on facebook (2011) IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, pp. 35-42Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet Classification with Deep Convolutional Neural Networks (2012) Advances in Neural Information Processing SystemsGeisler, W.S., Albrecht, D.G., Cortical neurons: Isolation of contrast gain control (1992) Vision Research, 32 (8), pp. 1409-1410Mendes, P.R., Neves, J.M.R., Tavares, A.I., Menotti, D., (2011) Vehicle License Plate Location (VLPL) Algorithms, , https://github.com/pedrormjunior/vlplMendes, P.R., Neves, J.M.R., Tavares, A.I., Menotti, D., Towards an automatic vehicle access control system: License plate location (2011) IEEE International Conference on Systems, Man, and Cybernetics (SMC, pp. 2916-2921Nomura, S., Yamanaka, K., Shiose, T., Kawakami, H., Katai, O., Morphological preprocessing method to thresholding degraded (2009) Word Images Pattern Recognition Letters, 30 (8), pp. 729-744(2013), https://github.com/omitted/vlpc, Omitted Vehicle license plate charactersBergstra, J., Bengio, Y., (2012) Random Search for Hyper-parameter Optimization, 13, pp. 281-305Palm, R.B., (2012) Prediction As A Candidate for Learning Deep Hierarchical Models of Data, , https://github.com/rasmusbergpalm/DeepLearnToolbox, Master's thesis, Technical University of Denmark, DTU Informatics, Lyngby, DenmarkGuo, J.-M., Liu, Y.-F., License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques (2008) IEEE Transactions on Vehicle Technology, 57 (3), pp. 1417-1424Comelli, P., Ferragina, P., Granieri, M.N., Stabile, F., Optical recognition of motor vehicle license plates (1995) IEEE Transactions on Vehicle Technology, 44 (4), pp. 790-799Coates, A., Ng, A.Y., Learning feature representations with Kmeans (2012) Neural Networks: Tricks of the Trade, pp. 561-580. , 2nd ed SpringerCoates, A., Ng, A., The importance of encoding versus training with sparse coding and vector quantization (2011) International Conference on Machine Learning (ICML, pp. 921-928Coates, A., Ng, A.Y., Lee, H., An analysis of single-layer networks in unsupervised feature learning (2011) Journal of Machine Learning Research-Proceedings Track, 15, pp. 215-22

A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems. (C) 2011 Elsevier Ltd. All rights reserved.376886894Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [2009/16206-1, 2010/00994-8

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection