Técnicas de extracción de características en imágenes para el reconocimiento de expresiones faciales

Las expresiones faciales son la manera más natural e inmediata en que los seres humanos comunican sus emociones. Para reconocer las expresiones faciales es fundamental realizar una buena extracción de características. En este trabajo se presentan diferentes técnicas de extracción de características, especialmente aquellas que se aplican en forma global u holística, se hace una comparación entre métodos, en términos de sus porcentajes de reconocimiento. Se describen las técnicas más usadas para esta tarea, como: seguimiento de movimientos, análisis espacial estadístico, y análisis espacio/frecuencia y se presentan los requerimientos que debe tener un sistema automático de reconocimiento de expresiones faciales

Computational Face Recognition Using Machine Learning Models

Faces are among the most complex stimuli that the human visual system processes. Growing commercial interest in face recognition is encouraging, but it also turns out to be a challenging endeavour. These challenges arise when the situations are complex and cause varied facial appearance due to e.g., occlusion, low-resolution, and ageing. The problem of computer-based face recognition using partial facial data is still largely an unexplored area of research and how does computer interpret various parts of the face. Another challenge is age progression and regression, which is considered to be the most revealing topic for understanding the human face changes during life. In this research, the various computational face recognition models are investigated to overcome the challenges posed by ageing and occlusions/partial faces. For partial face-based face recognition, a pre-trained VGGF model is employed for feature extraction and then followed by popular classifiers such as SVMs and Cosine Similarity CS for classification. In this framework, parts of faces such as eyes, nose, forehead, are used individually for training and testing. The results showing that there is an improvement in recognition in small parts, such as recognition rate in forehead enhanced form about 0% to nearly 35%, eyes from about 22% to approximately 65%. In the second framework, five sub-models were built based on Convolutional Neural Networks (CNNs) and those models are named Eyes-CNNs, Nose-CNNs, Mouth-CNNs, Forehead-CNNs, and combined EyesNose-CNNs. The experimental results illustrate a high recognition rate when it comes to small parts, for example, eyes increased up to about 90.83% and forehead reached about 44.5%. Furthermore, the challenge of face ageing is also approached by proposing an age-template based framework, generating an age-based face template for enhanced face generation and recognition. The results showing that generated new aged faces are more reliable comparing with state-of-the-art

Face recognition using skin texture

In today's society where information technology is depended upon throughout homes, educational establishments and workplaces the challenge of identity management is ever growing. Advancements in image processing and biometric feature based identification have provided a means for computer software to accurately identify individuals from increasingly vast databases of users. In the quest to improve the performance of such systems in varying environmental conditions skin texture is here proposed as a biometric feature. This thesis presents and discusses a hypothesis for the use of facial skin texture regions taken from 2-dimensional photographs to accurately identify individuals using three classifiers (neural network, support vector machine and linear discriminant). Gabor wavelet filters are primarily used for feature extraction and arc supported in later chapters by the grey-level cooccurrence probability matrix (GLCP) to strengthen the system by providing supplementary high-frequency features. Various fusion techniques for combining these features are presented and their perfonnance is compared including both score and feature fusion and various permutations of each. Based on preliminary results from the BioSecure Multimodal Database (BMDB) , the work presented indicates that isolated texture regions of the human face taken from under the eye may provide sufficient information to discriminately identify an individual with an equal error rate (EER) of under 1% when operating in greyscale. An analysis of the performance of the algorithm against image resolution investigates the systems performance when faced with lower resolution training images and discusses optimal resolutions for classifier training. The system also shows a good degree of robustness when the probe image resolution is reduced indicating that the algorithm provides some level of scale invariance. Scope for future work is laid out and a review of the evaluation is also presented

Recognizing Faces -- An Approach Based on Gabor Wavelets

As a hot research topic over the last 25 years, face recognition still seems to be a difficult and largely problem. Distortions caused by variations in illumination, expression and pose are the main challenges to be dealt with by researchers in this field. Efficient recognition algorithms, robust against such distortions, are the main motivations of this research. Based on a detailed review on the background and wide applications of Gabor wavelet, this powerful and biologically driven mathematical tool is adopted to extract features for face recognition. The features contain important local frequency information and have been proven to be robust against commonly encountered distortions. To reduce the computation and memory cost caused by the large feature dimension, a novel boosting based algorithm is proposed and successfully applied to eliminate redundant features. The selected features are further enhanced by kernel subspace methods to handle the nonlinear face variations. The efficiency and robustness of the proposed algorithm is extensively tested using the ORL, FERET and BANCA databases. To normalize the scale and orientation of face images, a generalized symmetry measure based algorithm is proposed for automatic eye location. Without the requirement of a training process, the method is simple, fast and fully tested using thousands of images from the BioID and BANCA databases. An automatic user identification system, consisting of detection, recognition and user management modules, has been developed. The system can effectively detect faces from real video streams, identify them and retrieve corresponding user information from the application database. Different detection and recognition algorithms can also be easily integrated into the framework

The use of high-throughput microscopy in the characterisation of phenotypes associated with the Unfolded Protein Response in Saccharomyces cerevisiae

Proteins traversing the secretory pathway begin their passage in the endoplasmic reticulum (ER) where they must be correctly folded and processed to pass quality control measures. Complications with this process can result in the accumulation of misfolded proteins, commonly referred to as ER-stress, which has been associated with a number of diseases. The unfolded protein response (UPR) is the cell’s mechanism of dealing with ER-stress and is activated via the IRE1-HAC1 pathway in yeast. Ire1p is the ER-stress sensor and upon recognising misfolded proteins Ire1 oligomerises and forms local clusters. Activated Ire1p then splices out an inhibitory intron from the UPR specific transcription factor Hac1p which goes on to initiate downstream responses to alleviate ER-stress. Here we utilise high-throughput microscopy and UPR-specific GFP reporter systems to characterise the UPR in the yeast Saccharomyces cerevisiae. High-throughput microscopy and automated image analysis is increasingly being used as a screening tool for investigating genome-wide collections of yeast strains, including the yeast deletion mutant array and the yeast GFP collection. We describe the use of GFP labelled Ire1p to visualise cluster formation as a reporter for early UPR recognition of misfolded proteins, as well as a GFP controlled by a Hac1p responsive promoter to measure downstream UPR activation. These UPR-specific GFP reporter systems were used to screen a collection of non-essential gene deletion strains, identifying gene deletions that induce UPR activation and thus are likely to function in the early secretory pathway. This included well known components such as the ALG members of the glycosylation pathway and various ER chaperones such as LHS1 and SCJ1. Additionally this analysis revealed 44 previously uncharacterised genes, suggesting there are still processes related to the secretory pathway that are yet to be described. Moreover, by inducing ER-stress in this screening system we revealed genes required for the normal activation of the UPR including ribosomal/translation and chromatin/transcriptionally related genes, as well as various genes from throughout the secretory pathway. Furthermore, we screened a collection of ~4000 strains, each expressing a different GFP fusion protein, under ER-stress conditions to identify protein expression and localisation changes induced by the UPR. Comparison to UPR deficient Δhac1 cells uncovered a set of UPR specific targets including 26 novel UPR targets that had not been identified in previous studies measuring changes at the transcript level. As part of this work, we developed a dual red fluorescent protein system to label cells for automated image segmentation to enable single cell phenotype measurements. Here we describe the use of texture analysis as a means of increasing automation in the identification of phenotypic changes across the proteome. These novel techniques may be more widely applied to screening GFP collections to increase automation of image analysis, particularly as manual annotation of phenotypic changes is a major bottleneck in high-throughput screening. The results presented here from microscopy based screening compare well with other techniques in the literature, but also provide new information highlighting the synergistic effects of integrating high-throughput imaging into traditional screening methodologies