10 research outputs found
Towards Automation and Human Assessment of Objective Skin Quantification
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
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An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms
There has been a significant increase in the interest for the task of classifying
demographic profiles i.e. race and ethnicity. Ethnicity is a significant human
characteristic and applying facial image data for the discrimination of ethnicity is
integral to face-related biometric systems. Given the diversity in the application
of ethnicity-specific information such as face recognition and iris recognition, and
the availability of image datasets for more commonly available human
populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians.
A gap has been identified for the development of a system which analyses the
full-face and its individual feature-components (eyes, nose and mouth), for the
Pakistani ethnic group. An efficient system is proposed for the verification of the
Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach.
Firstly, hand-crafted features were used to ascertain the descriptive nature of a
frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial
landmarks were selected (16 frontal and 10 for the profile) and by incorporating
2 models for redundant information removal, and a linear classifier for the binary
task. The experimental results concluded that the facial profile image of a
Pakistani face is distinct amongst other ethnicities. However, the methodology
consisted of limitations for example, low performance accuracy, the laborious
nature of manual data i.e. facial landmark, annotation, and the small facial image
dataset. To make the system more accurate and robust, Deep Learning models
are employed for ethnicity classification. Various state-of-the-art Deep models
are trained on a range of facial image conditions, i.e. full face and partial-face
images, plus standalone feature components such as the nose and mouth. Since
ethnicity is pertinent to the research, a novel facial image database entitled
Pakistani Face Database (PFDB), was created using a criterion-specific selection
process, to ensure assurance in each of the assigned class-memberships, i.e.
Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning
models was carried out on augmented image datasets, and the analysis
demonstrates that Deep Learning yields better performance accuracy compared
to low-level features. The human phase of the ethnicity classification framework
tested the discrimination ability of novice Pakistani and Non-Pakistani
participants, using a computerised ethnicity task. The results suggest that
humans are better at discriminating between Pakistani and Non-Pakistani full
face images, relative to individual face-feature components (eyes, nose, mouth),
struggling the most with the nose, when making judgements of ethnicity. To
understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii)
Single image procedure. The results concluded that participants perform
significantly better in trials where the target (Pakistani) image is shown alongside
a distractor (Non-Pakistani) image. To conclude the proposed framework,
directions for future study are suggested to advance the current understanding of
image based ethnicity verification.Acumé Forensi
Spatial Domain Representation for Face Recognition
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
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
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
顔画像からの顔姿勢と部位の検出と位置決めの研究
Tohoku University出口光一郎課
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
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