4 research outputs found
Experiments on deep face recognition using partial faces
YesFace recognition is a very current subject of great interest in the area of visual computing. In the past, numerous face recognition and authentication approaches have been proposed, though the great majority of them use full frontal faces both for training machine learning algorithms and for measuring the recognition rates. In this paper, we discuss some novel experiments to test the performance of machine learning, especially the performance of deep learning, using partial faces as training and recognition cues. Thus, this study sharply differs from the common approaches of using the full face for recognition tasks. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the forehead. In this study, we use a convolutional neural network based architecture along with the pre-trained VGG-Face model to extract features for training. We then use two classifiers namely the cosine similarity and the linear support vector machine to test the recognition rates. We ran our experiments on the Brazilian FEI dataset consisting of 200 subjects. Our results show that the cheek of the face has the lowest recognition rate with 15% while the (top, bottom and right) half and the 3/4 of the face have near 100% recognition rates.Supported in part by the European Union's Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035
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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
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The Biharmonic Eigenface
YesPrincipal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate
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A framework for facial age progression and regression using exemplar face templates
YesTechniques for facial age progression and regression have many applications and a myriad of challenges. As such, automatic aged or de-aged face generation has become an important subject of study in recent times. Over the past decade or so, researchers have been working on developing face processing mechanisms to tackle the challenge of generating realistic aged faces for applications related to smart systems. In this paper, we propose a novel approach to try and address this problem. We use template faces based on the formulation of an average face of a given ethnicity and for a given age. Thus, given a face image, the target aged image for that face is generated by applying it to the relevant template face image. The resulting image is controlled by two parameters corresponding to the texture and the shape of the face. To validate our approach, we compute the similarity between aged images and the corresponding ground truth via face recognition. To do this, we have utilised a pre-trained convolutional neural network based on the VGG-face model for feature extraction, and we then use well-known classifiers to compare the features. We have utilised two datasets, namely the FEI and the Morph II, to test, verify and validate our approach. Our experimental results do suggest that the proposed approach achieves accuracy, efficiency and possess flexibility when it comes to facial age progression or regression