Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda

Ensemble of Different Approaches for a Reliable Person Re-identification System

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/

Ensemble of convolutional neural networks for bioimage classification

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni

Sistema de verificação de impressão digital baseado em DWT, extração de recursos de vários domínios e classificador de subespaço de conjunto

This paper describes a fingerprint verification system including preprocessing, Wavelet transform, feature extraction using multiple domains, and ensemble subspace discriminant classifier. The system is implemented in MATLAB using Wavelet Toolbox, Image Processing Toolbox, and Statistics and Machine Learning Toolbox. First, the motivation and novelty, followed by the review of the previous work, are presented. Next, all steps are described in detail. Three fingerprint databases from the literature are used. The proposed method’s performance is compared with state-of-the-art techniques based on different classifiers utilizing the accuracy metric. The proposed algorithm achieves high accuracy at 97.5% for the DB3-FVC2000 subset.Este documento describe un sistema de verificación de huellas dactilares que incluye preprocesamiento, transformada Wavelet, extracción de características utilizando múltiples dominios y clasificador discriminante subespacial de conjunto. El sistema se implementa en MATLAB utilizando Wavelet Toolbox, Image Processing Toolbox y Statistics and Machine Learning Toolbox. En primer lugar, se presenta la motivación y la novedad, seguido de la revisión del trabajo anterior. A continuación, se describen todos los pasos en detalle. Se utilizan tres bases de datos de huellas dactilares de la literatura. El rendimiento del método propuesto se compara con técnicas de última generación basadas en diferentes clasificadores que utilizan la métrica de precisión. El algoritmo propuesto logra una alta precisión del 97,5 % para el subconjunto DB3-FVC2000.Este documento descreve um sistema de verificação de impressão digital que inclui pré-processamento, transformada Wavelet, extração de recursos usando vários domínios e classificador discriminante de subespaço de conjunto. O sistema é implementado em MATLAB usando Wavelet Toolbox, Image Processing Toolbox e Statistics and Machine Learning Toolbox. A motivação e a novidade são apresentadas primeiro, seguidas pela revisão do trabalho anterior. Todas as etapas são descritas em detalhes a seguir. Três bancos de dados de impressões digitais da literatura são usados. O desempenho do método proposto é comparado com técnicas do estado da arte baseadas em diferentes classificadores que utilizam a métrica de precisão. O algoritmo proposto atinge uma alta precisão de 97,5% para o subconjunto DB3-FVC2000

A new feature-based wavelet completed local ternary pattern (FEAT-WCLTP) for texture and medical image classification

Nowadays, texture image descriptors are used in many important real-life applications. The use of texture analysis in texture and medical image classification has attracted considerable attention. Local Binary Patterns (LBP) is one of the simplest yet eff ective texture descriptors. But it has some limitations that may affect its accuracy. Hence, different variants of LBP were proposed to overcome LBP’s drawbacks and enhance its classification accuracy. Completed local ternary pattern (CLTP) is one of the significant LBP variants. However, CLTP suffers from two main limitations: the selection of the threshold value is manually based and the high dimensionality which is negatively affected the descriptor performance and leads to high computations. This research aims to improve the classification accuracy of CLTP and overcome the computational limitation by proposing new descriptors inspired by CLTP. Therefore, this research introduces two contributions: The first one is a proposed new descriptor that integrates redundant discrete wavelet transform (RDWT) with the original CLTP, namely, wavelet completed local ternary pattern (WCLTP). Extracting CLTP in wavelet transform will help increase the classification accuracy due to the shift invariant property of RDWT. Firstly, the image is decomposed into four sub-bands (LL, LH, HL, HH) by using RDWT. Then, CLTP is extracted based on the LL wavelet coefficients. The latter one is the reduction in the dimensionality of WCLTP by reducing its size and a proposed new texture descriptor, namely, feature-based wavelet completed local ternary pattern (FeatWCLTP). The proposed Feat-WCLTP can enhance CLTP’s performance and reduce high dimensionality. The mean and variance of the values of the selected texture pattern are used instead of the normal magnitude texture descriptor of CLTP. The performance of the proposed WCLTP and Feat-WCLTP was evaluated using four textures (i.e. OuTex, CUReT, UIUC and Kylberg) and two medical (i.e. 2D HeLa and Breast Cancer) datasets then compared with several well-known LBP variants. The proposed WCLTP outperformed the previous descriptors and achieved the highest classification accuracy in all experiments. The results for the texture dataset are 99.35% in OuTex, 96.57% in CUReT, 94.80% in UIUC and 99.88% in the Kylberg dataset. The results for the medical dataset are 84.19% in the 2D HeLa dataset and 92.14% in the Breast Cancer dataset. The proposed Feat-WCLTP not only overcomes the dimensionality problem but also considerably improves the classification accuracy. The results for Feat-WCLTP for texture dataset are 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The results for the medical dataset are 84.42% in the 2D HeLa dataset and 89.12% in the Breast Cancer dataset. Moreover, the proposed Feat-WCLTP reduces the size of the feature vector for texture pattern (1,8) to 160 bins instead of 400 bins in WCLTP. The proposed WCLTP and Feat-WCLTP have better classification accuracy and dimensionality than the original CLTP

Speeded Up Robust Features Descriptor for Iris Recognition Systems

اكتسبت النظم البايومترية اهتماما كبيرا لعدة تطبيقات. كان تحديد القزحية أحد أكثر التقنيات البايومترية تطوراً للمصادقة الفعالة. نظام التعرف على القزحية الحالية يقدم نتائج دقيقة وموثوق بها على أساس الصور المأخوذة بالأشعة التحت الحمراء (NIR) عندما يتم التقاط الصور في مسافة ثابتة مع تعاون المستخدم. ولكن بالنسبة لصور العين الملونة التي تم الحصول عليها تحت الطول الموجي المرئي (VW) دون التعاون بين المستخدمين، فإن كفاءة التعرف على القزحية تتأثر بسبب الضوضاء مثل صور عدم وضوح العين، و تداخل الرموش ، والانسداد  بالأجفان وغيرها. يهدف هذا العمل إلى استخدام (SURF) لاسترداد خصائص القزحية في كل من صور قزحية NIR والطيف المرئي. يتم استخدام هذا النهج وتقييمه على قواعد بيانات CASIA v1and IITD v1 كصورة قزحية NIR وUBIRIS v1 كصورة ملونة. وأظهرت النتائج معدل دقة عالية (98.1 ٪) على CASIA v1, (98.2) على IITD v1 و (83٪) على UBIRIS v1 تقييمها بالمقارنة مع الأساليب الأخرى.Biometric systems have gained significant attention for several applications. Iris identification was one of the most sophisticated biometrical techniques for effective and confident authentication. Current iris identification system offers accurate and reliable results based on near- infra -red light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without cooperation between the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion and reflection. This works aims to use Speeded up robust features Descriptor (SURF) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. This approach is used and evaluated on the CASIA v1and IITD v1 databases as NIR iris image and UBIRIS v1 as color image. The evaluation results showed a high accuracy rate 98.1 % on CASIA v1, 98.2 on IITD v1 and 83% on UBIRIS v1 evaluated by comparing to the other method

Improving Facial Emotion Recognition with Image processing and Deep Learning

Humans often use facial expressions along with words in order to communicate effectively. There has been extensive study of how we can classify facial emotion with computer vision methodologies. These have had varying levels of success given challenges and the limitations of databases, such as static data or facial capture in non-real environments. Given this, we believe that new preprocessing techniques are required to improve the accuracy of facial detection models. In this paper, we propose a new yet simple method for facial expression recognition that enhances accuracy. We conducted our experiments on the FER-2013 dataset that contains static facial images. We utilized Unsharp Mask and Histogram equalization to emphasize texture and details of the images. We implemented Convolution Neural Networks [CNNs] to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. We also employed pre-trained models such as Resnet-50, Senet-50, VGG16, and FaceNet, and applied transfer learning to achieve an accuracy of 76.01% using an ensemble of seven models

A dynamic texture based approach to recognition of facial actions and their temporal models

In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set