54,007 research outputs found
Face Prediction Model for an Automatic Age-invariant Face Recognition System
Automated face recognition and identification softwares are becoming part of
our daily life; it finds its abode not only with Facebook's auto photo tagging,
Apple's iPhoto, Google's Picasa, Microsoft's Kinect, but also in Homeland
Security Department's dedicated biometric face detection systems. Most of these
automatic face identification systems fail where the effects of aging come into
the picture. Little work exists in the literature on the subject of face
prediction that accounts for aging, which is a vital part of the computer face
recognition systems. In recent years, individual face components' (e.g. eyes,
nose, mouth) features based matching algorithms have emerged, but these
approaches are still not efficient. Therefore, in this work we describe a Face
Prediction Model (FPM), which predicts human face aging or growth related image
variation using Principle Component Analysis (PCA) and Artificial Neural
Network (ANN) learning techniques. The FPM captures the facial changes, which
occur with human aging and predicts the facial image with a few years of gap
with an acceptable accuracy of face matching from 76 to 86%.Comment: 3 pages, 2 figure
Age Invariant Face Recognition using Convolutional Neural Network
In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition. Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier
Recognising the ageing face: the role of age in face processing
The effects of age-induced changes on face recognition were investigated as a means of exploring the role of age in the encoding of new facial memories. The ability of participants to recognise each of six previously learnt faces was tested with versions which were either identical to the learnt faces, the same age (but different in pose and expression), or younger or older in age. Participants were able to cope well with facial changes induced by ageing: their performance with older, but not younger, versions was comparable to that with faces which differed only in pose and expression. Since the large majority of different age versions were recognised successfully, it can be concluded that the process of recognition does not require an exact match in age characteristics between the stored representation of a face and the face currently in view. As the age-related changes explored here were those that occur during the period of growth, this in turn implies that the underlying structural physical properties of the face are (in addition to pose and facial expression) invariant to a certain extent
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
To minimize the impact of age variation on face recognition, age-invariant
face recognition (AIFR) extracts identity-related discriminative features by
minimizing the correlation between identity- and age-related features while
face age synthesis (FAS) eliminates age variation by converting the faces in
different age groups to the same group. However, AIFR lacks visual results for
model interpretation and FAS compromises downstream recognition due to
artifacts. Therefore, we propose a unified, multi-task framework to jointly
handle these two tasks, termed MTLFace, which can learn the age-invariant
identity-related representation for face recognition while achieving pleasing
face synthesis for model interpretation. Specifically, we propose an
attention-based feature decomposition to decompose the mixed face features into
two uncorrelated components -- identity- and age-related features -- in a
spatially constrained way. Unlike the conventional one-hot encoding that
achieves group-level FAS, we propose a novel identity conditional module to
achieve identity-level FAS, which can improve the age smoothness of synthesized
faces through a weight-sharing strategy. Benefiting from the proposed
multi-task framework, we then leverage those high-quality synthesized faces
from FAS to further boost AIFR via a novel selective fine-tuning strategy.
Furthermore, to advance both AIFR and FAS, we collect and release a large
cross-age face dataset with age and gender annotations, and a new benchmark
specifically designed for tracing long-missing children. Extensive experimental
results on five benchmark cross-age datasets demonstrate that MTLFace yields
superior performance for both AIFR and FAS. We further validate MTLFace on two
popular general face recognition datasets, obtaining competitive performance on
face recognition in the wild. Code is available at
http://hzzone.github.io/MTLFace.Comment: TPAMI 2022. arXiv admin note: substantial text overlap with
arXiv:2103.0152
Age invariant face recognition system using automated voronoi diagram segmentation
One of the challenges in automatic face recognition is to achieve sequential
face invariant. This is a challenging task because the human face undergoes many
changes as a person grows older. In this study we will be focusing on age invariant
features of a human face. The goal of this study is to investigate the face age invariant
features that can be used for face matching, secondly is to come out with a prototype
of matching scheme that is robust to the changes of facial aging and finally to
evaluate the proposed prototype with the other similar prototype. The proposed
approach is based on automated image segmentation using Voronoi Diagram (VD)
and Delaunay Triangulations (DT). Later from the detected face region, the eyes will
be detected using template matching together with DT. The outcomes, which are list
of five coordinates, will be used to calculate interest distance in human faces. Later
ratios between those distances are formulated. Difference vector will be use in the
proposed method in order to perform face recognition steps. Datasets used for this
research is selected images from FG-NET Aging Database and BioID Face Database,
which is widely being used for image based face aging analysis; consist of 15 sample
images taken from 5 different person. The selection is based on the project scopes
and difference ages. The result shows that 11 images are successfully recognized. It
shows an increase to 73.34% compared to other recent methods
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