10 research outputs found

    Towards Automation and Human Assessment of Objective Skin Quantification

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    The goal of this study is to provide an objective criterion for computerised skin quality assessment. Humans have been impacted by a variety of face features. Utilising eye-tracking technology assists to get a better understanding of human visual behaviour, this research examined the influence of face characteristics on the quantification of skin evaluation and age estimation. The results revealed that when facial features are apparent, individuals do well in age estimation. Also, this research attempts to examine the performance and perception of machine learning algorithms for various skin attributes. Comparison of the traditional machine learning technique to deep learning approaches. Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) were used to evaluate classification algorithms, with CNNs outperforming SVM. The primary difficulty in training deep learning algorithms is the need of large-scale dataset. This thesis proposed two high-resolution face datasets to address the requirement of face images for research community to study face and skin quality. Additionally, the study of machine-generated skin patches using Generative Adversarial Networks (GANs) is conducted. Dermatologists confirmed the machine-generated images by evaluating the fake and real images. Only 38% accurately predicted the real from fake correctly. Lastly, the performance of human perception and machine algorithm is compared using the heat-map from the eye-tracking experiment and the machine learning prediction on age estimation. The finding indicates that both humans and machines predict in a similar manner

    Spatial Domain Representation for Face Recognition

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    Spatial domain representation for face recognition characterizes extracted spatial facial features for face recognition. This chapter provides a complete understanding of well-known and some recently explored spatial domain representations for face recognition. Over last two decades, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) and local binary patterns (LBP) have emerged as promising spatial feature extraction techniques for face recognition. SIFT and HOG are effective techniques for face recognition dealing with different scales, rotation, and illumination. LBP is texture based analysis effective for extracting texture information of face. Other relevant spatial domain representations are spatial pyramid learning (SPLE), linear phase quantization (LPQ), variants of LBP such as improved local binary pattern (ILBP), compound local binary pattern (CLBP), local ternary pattern (LTP), three-patch local binary patterns (TPLBP), four-patch local binary patterns (FPLBP). These representations are improved versions of SIFT and LBP and have improved results for face recognition. A detailed analysis of these methods, basic results for face recognition and possible applications are presented in this chapter

    Face recognition in uncontrolled environments

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    This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories. Compared with controlled environments, images from uncontrolled environments contain more variation in pose, lighting, expression, occlusion, background, image quality, scale, and makeup. Therefore, face recognition in uncontrolled environments is much more challenging than in controlled conditions. Moreover, many real world applications require good recognition performance in uncontrolled environments. Example applications include social networking, human-computer interaction and electronic entertainment. Therefore, researchers and companies have shifted their interest from controlled environments to uncontrolled environments over the past seven years. In this thesis, we divide the history of face recognition into four stages and list the main problems and algorithms at each stage. We find that face recognition in unconstrained environments is still an unsolved problem although many face recognition algorithms have been proposed in the last decade. Existing approaches have two major limitations. First, many methods do not perform well when tested in uncontrolled databases even when all the faces are close to frontal. Second, most current algorithms cannot handle large pose variation, which has become a bottleneck for improving performance. In this thesis, we investigate Bayesian models for face recognition. Our contributions extend Probabilistic Linear Discriminant Analysis (PLDA) [Prince and Elder 2007]. In PLDA, images are described as a sum of signal and noise components. Each component is a weighted combination of basis functions. We firstly investigate the effect of degree of the localization of these basis functions and find better performance is obtained when the signal is treated more locally and the noise more globally. We call this new algorithm multi-scale PLDA and our experiments show it can handle lighting variation better than PLDA but fails for pose variation. We then analyze three existing Bayesian face recognition algorithms and combine the advantages of PLDA and the Joint Bayesian Face algorithm [Chen et al. 2012] to propose Joint PLDA. We find that our new algorithm improves performance compared to existing Bayesian face recognition algorithms. Finally, we propose Tied Joint Bayesian Face algorithm and Tied Joint PLDA to address large pose variations in the data, which drastically decreases performance in most existing face recognition algorithms. To provide sufficient training images with large pose difference, we introduce a new database called the UCL Multi-pose database. We demonstrate that our Bayesian models improve face recognition performance when the pose of the face images varies

    The influence of imagination, connectivity, and social context on the assessment and measurement of empathic accuracy using photographic stimuli

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    The ability to accurately interpret the emotions of others is known as empathic accuracy, and in this thesis is referred to as Affect Recognition-Empathic Accuracy (AR-EA). This ability can facilitate pro-social behaviours while deficits may result in anti-social behaviours. Research has demonstrated that imagination, connectivity, and social context can all influence our ability to accurately interpret the emotions of others; however, there has been little research investigating how these specific factors might be enhanced, or influence AR-EA abilities when using photographic stimuli. There were two aims to this thesis. The first aim was to investigate the possibility of inserting specific empathy related elements, imagination, connectivity, and social context, into a set of photographic stimuli to assess the potential influence on AR-EA. The second aim was to develop an original set of photographic stimuli for use in this thesis, and to conduct psychometric evaluations on said photographs in order to develop a new photographic measure for the assessment and evaluation of AR-EA. The photographs consisted of both male and female models expressing six different basic emotions (happy, sad, fear, anger, surprise, disgust) at three different levels of intensity (low, medium and high intensity), plus one neutral expression. Imagination and connectivity were both facilitated through the insertion of a silhouette (blacked out full body figure, male or female) into the photographic stimuli. Social context was manipulated through the use of different social setting backgrounds in the photographs: a kitchen, a bar (as in a tavern), and a neutral background. Results demonstrated the silhouette inserted into the photographs to facilitate imagination and connectivity not only enhanced empathic processes, but also produced photographic-based measure of AR-EA that was superior in both reliability and validity to other presentation modes (full body only, and head and shoulders only stimuli). The different social settings of the photographs also impacted AR-EA abilities facilitating the accurate interpretation of some emotions, whilst inhibiting others. The overall findings of this thesis question past research methods as well as provide intriguing insights into the functioning of empathic accuracy processes which have not been previously reported. The testing and research also resulted in a new photographic measure for the assessment of AR-EA abilities, whilst the use of simple techniques to manipulate empathy-based elements within the photographs offers new opportunities for future research

    Local Feature Based Face Recognition

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    画像情報を利用した複数識別統合による性別と年齢層の識別

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    制度:新 ; 文部省報告番号:甲2483号 ; 学位の種類:博士(工学) ; 授与年月日:2007/7/26 ; 早大学位記番号:新459

    Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB

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    The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

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