A multimodal deep learning framework using local feature representations for face recognition

YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets

Face Recognition Using Gabor-based Improved Supervised Locality Preserving Projections

A novel Gabor-based Improved Supervised Locality Preserving Projections for face recognition is presented in this paper. This new algorithm is based on a combination of Gabor wavelets representation of face images and Improved Supervised Locality Preserving Projections for face recognition and it is robust to changes in illumination and facial expressions and poses. In this paper, Gabor filter is first designed to extract the features from the whole face images, and then a supervised locality preserving projections, which is improved by two-directional 2DPCA to eliminate redundancy among Gabor features, is used to augment these Gabor feature vectors derived from Gabor wavelets representation. The new algorithm benefits mostly from two aspects: One aspect is that Gabor wavelets are promoted for their useful properties, such as invariance to illumination, rotation, scale and translations, in feature extraction. The other is that the Improved Supervised Locality Preserving Projections not only provides a category label for each class in a training set, but also reduces more coefficients for image representation from two directions and boost the recognition speed. Experiments based on the ORL face database demonstrate the effectiveness and efficiency of the new method. Results show that our new algorithm outperforms the other popular approaches reported in the literature and achieves a much higher accurate recognition rate

Local And Semi-Global Feature-Correlative Techniques For Face Recognition

Face recognition is an interesting field of computer vision with many commercial and scientific applications. It is considered as a very hot topic and challenging problem at the moment. Many methods and techniques have been proposed and applied for this purpose, such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as well as its points of strength. This paper introduces a highly efficient method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in different views (poses) of facial images. Feature extraction techniques are applied on the images (faces) based on Zernike moments and structural similarity measure (SSIM) with local and semi-global blocks. Pre-processing is carried out whenever needed, and numbers of measurements are derived. More specifically, instead of the usual approach for applying statistics or structural methods only, the proposed methodology integrates higher-order representation patterns extracted by Zernike moments with a modified version of SSIM (M-SSIM). Individual measurements and metrics resulted from mixed SSIM and Zernike-based approaches give a powerful recognition tool with great results. Experiments reveal that correlative Zernike vectors give a better discriminant compared with using 2D correlation of the image itself. The recognition rate using ORL Database of Faces reaches 98.75%, while using FEI (Brazilian) Face Database we got 96.57%. The proposed approach is robust against rotation and noise

A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms

yesn this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches

3D Dynamic Expression Recognition Based on a Novel Deformation Vector Field and Random Forest

International audienceThis paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition from 3D dynamic faces. By applying LDA-based feature space transformation for dimensionality reduction which is followed by a Multiclass Random Forest learning algorithm, the proposed approach achieved 93% average recognition rate on BU-4DFE database and outperforms state-of-art approaches

Features Mapping Based Human Gait Recognition

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS. DOI: 10.17762/ijritcc2321-8169.15067

Convolutional neural network extreme learning machine for effective classification of hyperspectral images

Due to its excellent performance in terms of fast implementation, strong generalization capability and straightforward solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) classification. However, the performance of ELM for HSI classification remains a challenging problem especially in effective extraction of the featured information from the massive volume of data. To this end, we propose in this paper a new method to combine Convolutional neural network (CNN) with ELM (CNN-ELM) for HSI classification. As CNN has been successfully applied for feature extraction in different applications, the combined CNN-ELM approach aims to take advantages of these two techniques for improved classification of HSI. By preserving the spatial features whilst reconstructing the spectral features of HSI, the proposed CNN-ELM method can significantly improve the accuracy of HSI classification without increasing the computational complexity. Comprehensive experiments using three publicly available HSI data sets, Pavia University, Pavia center, and Salinas have fully validated the improved performance of the proposed method when benchmarking with several state-of-the-art approaches

Features for matching people in different views

There have been significant advances in the computer vision field during the last decade. During this period, many methods have been developed that have been successful in solving challenging problems including Face Detection, Object Recognition and 3D Scene Reconstruction. The solutions developed by computer vision researchers have been widely adopted and used in many real-life applications such as those faced in the medical and security industry. Among the different branches of computer vision, Object Recognition has been an area that has advanced rapidly in recent years. The successful introduction of approaches such as feature extraction and description has been an important factor in the growth of this area. In recent years, researchers have attempted to use these approaches and apply them to other problems such as Content Based Image Retrieval and Tracking. In this work, we present a novel system that finds correspondences between people seen in different images. Unlike other approaches that rely on a video stream to track the movement of people between images, here we present a feature-based approach where we locate a target’s new location in an image, based only on its visual appearance. Our proposed system comprises three steps. In the first step, a set of features is extracted from the target’s appearance. A novel algorithm is developed that allows extraction of features from a target that is particularly suitable to the modelling task. In the second step, each feature is characterised using a combined colour and texture descriptor. Inclusion of information relating to both colour and texture of a feature add to the descriptor’s distinctiveness. Finally, the target’s appearance and pose is modelled as a collection of such features and descriptors. This collection is then used as a template that allows us to search for a similar combination of features in other images that correspond to the target’s new location. We have demonstrated the effectiveness of our system in locating a target’s new position in an image, despite differences in viewpoint, scale or elapsed time between the images. The characterisation of a target as a collection of features also allows our system to robustly deal with the partial occlusion of the target

Combining local descriptors and classification methods for human emotion recognition.

Masters Degree. University of KwaZulu-Natal, Durban.Human Emotion Recognition occupies a very important place in artificial intelligence and has several applications, such as emotionally intelligent robots, driver fatigue monitoring, mood prediction, and many others. Facial Expression Recognition (FER) systems can recognize human emotions by extracting face image features and classifying them as one of several prototypic emotions. Local descriptors are good at encoding micro-patterns and capturing their distribution in a sub-region of an image. Moreover, dividing the face into sub-regions introduces information about micro-pattern locations, essential for developing robust facial expression features. Hence, local descriptors’ efficiencies depend heavily on parameters such as the sub-region size and histogram length. However, the extraction parameters are seldom optimized in existing approaches. This dissertation reviews several local descriptors and classifiers, and experiments are conducted to improve the robustness and accuracy of existing FER methods. A study of the Histogram of Oriented Gradients (HOG) descriptor inspires this research to propose a new face registration algorithm. The approach uses contrast-limited histogram equalization to enhance the image, followed by binary thresholding and blob detection operations to rotate the face upright. Additionally, this research proposes a new method for optimized FER. The main idea behind the approach is to optimize the calculation of feature vectors by varying the extraction parameter values, producing several feature sets. The best extraction parameter values are selected by evaluating the classification performances of each feature set. The proposed approach is also implemented using different combinations of local descriptors and classification methods under the same experimental conditions. The results reveal that the proposed methods produced a better performance than what was reported in previous studies. Furthermore, the results showed an improvement of up to 2% compared with the performance achieved in previous works. The results showed that HOG was the most effective local descriptor, while Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) were the best classifiers. Hence, the best combinations were HOG+SVM and HOG+MLP