Binary Pattern Analysis for 3D Facial Action Unit Detection

In this paper we propose new binary pattern features for use in the problem of 3D facial action unit (AU) detection. Two representations of 3D facial geometries are employed, the depth map and the Azimuthal Projection Distance Image (APDI). To these the traditional Local Binary Pattern is applied, along with Local Phase Quantisation, Gabor filters and Monogenic filters, followed by the binary pattern feature extraction method. Feature vectors are formed for each feature type through concatenation of histograms formed from the resulting binary numbers. Feature selection is then performed using a two-stage GentleBoost approach. Finally, we apply Support Vector Machines as classifiers for detection of each AU. This system is tested in two ways. First we perform 10-fold cross-validation on the Bosphorus database, and then we perform cross-database testing by training on this database and then testing on apex frames from the D3DFACS database, achieving promising results in both

Innovative local texture descriptors with application to eye detection

Local Binary Patterns (LBP), which is one of the well-known texture descriptors, has broad applications in pattern recognition and computer vision. The attractive properties of LBP are its tolerance to illumination variations and its computational simplicity. However, LBP only compares a pixel with those in its own neighborhood and encodes little information about the relationship of the local texture with the features. This dissertation introduces a new Feature Local Binary Patterns (FLBP) texture descriptor that can compare a pixel with those in its own neighborhood as well as in other neighborhoods and encodes the information of both local texture and features. The features encoded in FLBP are broadly defined, such as edges, Gabor wavelet features, and color features. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image is derived. The feasibility of the proposed FLBP is demonstrated on eye detection using the BioID and the FERET databases. Experimental results show that the FLBP method significantly improves upon the LBP method in terms of both the eye detection rate and the eye center localization accuracy. As LBP is sensitive to noise especially in near-uniform image regions, Local Ternary Patterns (LTP) was proposed to address this problem by extending LBP to three-valued codes. However, further research reveals that both LTP and LBP achieve similar results for face and facial expression recognition, while LTP has a higher computational cost than LBP. To improve upon LTP, this dissertation introduces another new local texture descriptor: Local Quaternary Patterns (LQP) and its extension, Feature Local Quaternary Patterns (FLQP). LQP encodes four relationships of local texture, and therefore, it includes more information of local texture than the LBP and the LTP. FLQP, which encodes both local and feature information, is expected to perform even better than LQP for texture description and pattern analysis. The LQP and FLQP are applied to eye detection on the BioID database. Experimental results show that both FLQP and LQP achieve better eye detection performance than FLTP, LTP, FLBP and LBP. The FLQP method achieves the highest eye detection rate

Reconhecimento de expressões faciais compostas em imagens 3D : ambiente forçado vs ambiente espontâneo

Orientadora: Profa. Dra. Olga Regina Pereira BellonDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 16/12/2017Inclui referências: p.56-60Área de concentração: Ciência da ComputaçãoResumo: Neste trabalho, realiza-se o reconhecimento de Expressões Faciais Compostas (EFCs), em imagens 3D, nos ambientes de captura: forçado e espontâneo. Explora-se assim, uma moderna categorização de expressões faciais, diferente das expressões faciais básicas, por ser construída pela combinação de duas expressões básicas. A pesquisa se orienta através da utilização de imagens 3D por conta de suas vantagens intrínsecas: não apresentam problemas decorrentes de variações de pose, iluminação e de outras mudanças na aparência facial. Consideram-se dois ambientes de captura de expressões: forçado (quando o sujeito é instruído para realizar a expressão) e espontâneo (quando o sujeito produz a expressão por meio de estímulos). Isto, com a intenção de comparar o comportamento dos dois em relação ao reconhecimento de EFCs, já que, diferem em várias dimensões, incluindo dentre elas: complexidade, temporalidade e intensidade. Por fim, propõe-se um método para reconhecer EFCs. O método em questão representa uma nova aplicação de detectores de movimentos dos músculos faciais já existentes. Esses movimentos faciais detectar são denotados no sistema de codificação de ação facial (FACS) como Unidades de Ação (AUs). Consequentemente, implementam-se detectores de AUs em imagens 3D baseados em padrões binários locais de profundidade (LDBP). Posteriormente, o método foi aplicado em duas bases de dados públicas com imagens 3D: Bosphorus (ambiente forçado) e BP4D-Spontaneus (ambiente espontâneo). Nota-se que o método desenvolvido não diferencia as EFCs que apresentam a mesma configuração de AUs, sendo estas: "felicidade com nojo", "horror" e "impressão", por conseguinte, considera-se essas expressões como um "caso especial". Portanto, ponderaram-se 14 EFCs, mais o "caso especial" e imagens sem EFCs. Resultados obtidos evidenciam a existência de EFCs em imagens 3D, das quais aproveitaramse algumas características. Além disso, notou-se que o ambiente espontâneo, teve melhor comportamento em reconhecer EFCs tanto pelas AUs anotadas na base, quanto pelas AUs detectadas automaticamente; reconhecendo mais casos de EFCs e com melhor desempenho. Pelo nosso conhecimento, esta é a primeira vez que EFCs são investigadas em imagens 3D. Palavras-chave: Expressões faciais compostas, FACS, Detecção de AUs, Ambiente forçado, Ambiente espontâneo.Abstract: The following research investigates Compound Facial Expressions (EFCs) in 3D images captured in the domains: forced and spontaneous. The work explores a modern categorization of facial expressions, different than basic facial expressions, but constructed by the combination of two basic categories of emotion. The investigation used 3D images because of their intrinsic advantages: they do not present problems due to variations in pose, lighting and other changes in facial appearance. For this purpose, this research considers both forced (when the subject is instructed to perform the expression) and spontaneous (when the subject produces the expression by means of stimuli) expression caption domains. This has the intention of comparing the behavior of both domains by analyzing the recognition of EFCs, because they differ in many dimentions, including complexity, time and intensity. Finally, a method for EFCs recognition is proposed. The method in question represents a new application of existing detectors of facial muscle movements. These facial movimentes to detect are denoted in the Facial Action Coding System (FACS) as Action Units (AUs). Consequently, 3D Facial AUs Detectors are developed based on Local Depth Binary Patterns (LDBP). Subsequently, the method was applied to two public databases with 3D images: Bosphorus (forced domain) and BP4D-Spontaneous (spontaneous domain). Note that the developed method does not differentiate the EFCs that present the same AU configuration: "sadly disgusted", "appalled" and "hateful", therefore, these expressions are considered a "special case". Thus, 14 EFCs are observed, plus the "special case" and the non-EFCs images. The results confirm the existence of EFCs in 3D images, from which some characteristics were exploit. In addition, noticed that the spontaneous environment was better at recognizing EFCs by the AUs annotated at the database and by the AUs detected; recognizing more cases of EFCs and with better performance. From our best knowledge, this is the first time that EFCs are explored for 3D images. Keywords: Coumpound facial expression, FACS, AUs detection, posed domain, spontaneous domain

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

BRUISE DETECTION IN APPLES USING 3D INFRARED IMAGING AND MACHINE LEARNING TECHNOLOGIES

Bruise detection plays an important role in fruit grading. A bruise detection system capable of finding and removing damaged products on the production lines will distinctly improve the quality of fruits for sale, and consequently improve the fruit economy. This dissertation presents a novel automatic detection system based on surface information obtained from 3D near-infrared imaging technique for bruised apple identification. The proposed 3D bruise detection system is expected to provide better performance in bruise detection than the existing 2D systems. We first propose a mesh denoising filter to reduce noise effect while preserving the geometric features of the meshes. Compared with several existing mesh denoising filters, the proposed filter achieves better performance in reducing noise effect as well as preserving bruised regions in 3D meshes of bruised apples. Next, we investigate two different machine learning techniques for the identification of bruised apples. The first technique is to extract hand-crafted feature from 3D meshes, and train a predictive classifier based on hand-crafted features. It is shown that the predictive model trained on the proposed hand-crafted features outperforms the same models trained on several other local shape descriptors. The second technique is to apply deep learning to learn the feature representation automatically from the mesh data, and then use the deep learning model or a new predictive model for the classification. The optimized deep learning model achieves very high classification accuracy, and it outperforms the performance of the detection system based on the proposed hand-crafted features. At last, we investigate GPU techniques for accelerating the proposed apple bruise detection system. Specifically, the dissertation proposes a GPU framework, implemented in CUDA, for the acceleration of the algorithm that extracts vertex-based local binary patterns. Experimental results show that the proposed GPU program speeds up the process of extracting local binary patterns by 5 times compared to a single-core CPU program