485 research outputs found

    Intelligent facial emotion recognition using moth-firefly optimization

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    In this research, we propose a facial expression recognition system with a variant of evolutionary firefly algorithm for feature optimization. First of all, a modified Local Binary Pattern descriptor is proposed to produce an initial discriminative face representation. A variant of the firefly algorithm is proposed to perform feature optimization. The proposed evolutionary firefly algorithm exploits the spiral search behaviour of moths and attractiveness search actions of fireflies to mitigate premature convergence of the Levy-flight firefly algorithm (LFA) and the moth-flame optimization (MFO) algorithm. Specifically, it employs the logarithmic spiral search capability of the moths to increase local exploitation of the fireflies, whereas in comparison with the flames in MFO, the fireflies not only represent the best solutions identified by the moths but also act as the search agents guided by the attractiveness function to increase global exploration. Simulated Annealing embedded with Levy flights is also used to increase exploitation of the most promising solution. Diverse single and ensemble classifiers are implemented for the recognition of seven expressions. Evaluated with frontal-view images extracted from CK+, JAFFE, and MMI, and 45-degree multi-view and 90-degree side-view images from BU-3DFE and MMI, respectively, our system achieves a superior performance, and outperforms other state-of-the-art feature optimization methods and related facial expression recognition models by a significant margin

    Combining local descriptors and classification methods for human emotion recognition.

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    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

    FACE RECOGNITION METHOD USING GABOR WAVELETS (GWS)

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    Facial features can be used to recognize and identify the characteristics of a person. In this project, Gabor Wavelet (GW) based recognition technique is proposed whereby the GW is used to extract the facial feature of a person. The face recognition system consist of four (4) major stages namely image preprocessing, feature extraction, matching technique and classification technique. In feature extraction stage, the input images are converted into grayscale image prior to applying the 2D GWs. The resulting feature vectors are used to test the similarity score with the feature vectors of the facial image in the database

    High Order Volumetric Directional Pattern for Video-Based Face Recognition

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    Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches

    Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing

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    Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99

    Age Invariant Face Recognition using Convolutional Neural Network

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    In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier

    Methods for multi-spectral image fusion: identifying stable and repeatable information across the visible and infrared spectra

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    Fusion of images captured from different viewpoints is a well-known challenge in computer vision with many established approaches and applications; however, if the observations are captured by sensors also separated by wavelength, this challenge is compounded significantly. This dissertation presents an investigation into the fusion of visible and thermal image information from two front-facing sensors mounted side-by-side. The primary focus of this work is the development of methods that enable us to map and overlay multi-spectral information; the goal is to establish a combined image in which each pixel contains both colour and thermal information. Pixel-level fusion of these distinct modalities is approached using computational stereo methods; the focus is on the viewpoint alignment and correspondence search/matching stages of processing. Frequency domain analysis is performed using a method called phase congruency. An extensive investigation of this method is carried out with two major objectives: to identify predictable relationships between the elements extracted from each modality, and to establish a stable representation of the common information captured by both sensors. Phase congruency is shown to be a stable edge detector and repeatable spatial similarity measure for multi-spectral information; this result forms the basis for the methods developed in the subsequent chapters of this work. The feasibility of automatic alignment with sparse feature-correspondence methods is investigated. It is found that conventional methods fail to match inter-spectrum correspondences, motivating the development of an edge orientation histogram (EOH) descriptor which incorporates elements of the phase congruency process. A cost function, which incorporates the outputs of the phase congruency process and the mutual information similarity measure, is developed for computational stereo correspondence matching. An evaluation of the proposed cost function shows it to be an effective similarity measure for multi-spectral information
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