3 research outputs found
An improved age invariant face recognition using data augmentation
In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different
techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging dataset was adopted to enable the benchmarking of experimental
results. The FG-Net dataset was augmented by adding four different types of noises at the preprocessing phase in order to improve the trait aging face features extraction and the training model used at the classification stages,
thus addressing the problem of few available training aging for face recognition dataset. The developed model was an adaptation of a pre-trained convolution neural network architecture (Inception-ResNet-v2) which is a very robust noise. The proposed model on testing achieved a 99.94%
recognition accuracy, a mean square error of 0.0158 and a mean absolute error of 0.0637. The results obtained are significant improvements in comparison with related works
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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
Feature-aging for age-invariant face recognition
Age-invariant face recognition has attracted some recent attention. In real applications, the age progression of those face images, stored in a face database for recognition and identification purposes, should also be considered, so as to achieve a higher accuracy level. In this paper, we propose a method to predict the aging of facial features so as to alleviate the effect of age progression on face recognition. The original facial feature and the aged facial feature of a face image should be correlated, so they are fused by using canonical correlation analysis to form a coherent feature for face recognition. The performance of our proposed approach is evaluated based on the FGNet database, and compared to some existing face recognition algorithms. Experiment results show that our proposed method can achieve a superior performance, when the query and probe face images have a large age difference.Department of Electronic and Information EngineeringRefereed conference pape