Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Bouchaffra, Djamel

doi:10.1109/tnnls.2014.2341634

Cited by 33 publications

(8 citation statements)

References 26 publications

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“…The results are tested on FGNET with 980 images of all 82 subjects, on MORPH (album II) with 1005 (Frontal and Non-frontal) and 2084 (Only Frontal) images. [17] 48.96 Facial Asymmetry [12] 69.40 Graph based view [35] 64.47 NTCA [17] 83.80 MDL [36] 65.2 HFA [38] 91.14 PCA & WLBP [37] 67.30 MDL [36] 91.8 HFA [38] 69.0 CNN [24] 92.2 Facial Asymmetry [12] 69.51 MEFD [39] 92.26 MEFD [37] 76. From the experimentations, it is observed that, a. CNN is better in Rank-1 recognition than available methods with no complicated preprocessing steps like histogram normalization and head pose correction.…”

Section: Overall Comparative Discussionmentioning

confidence: 99%

“…They adopted phase congruency feature for shape and LBP variance for texture feature. Bouchaffra [17] introduced a novel framework to reduce dimensionality and extracting topological features such as shape for age invariant face recognition. It is a combination of Kernelized Radial basis function (KRBF) for dimensionality reduction, construction of α-shape for feature extraction and mixture multinomial distributions for object classification.…”

Section: Discriminativemmentioning

confidence: 99%

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Age Invariant Face Recognition using Convolutional Neural Network

Nimbarte¹,

Bhoyar²

2018

IJECE

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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.

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Section: Overall Comparative Discussionmentioning

confidence: 99%

Section: Discriminativemmentioning

confidence: 99%

Age Invariant Face Recognition using Convolutional Neural Network

Nimbarte¹,

Bhoyar²

2018

IJECE

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“…Head-pose estimation is challenging because it must cope with changing illumination conditions, variability in face orientation and appearance, partial occlusions of facial landmarks, as well as bounding-box to-face alignment errors. This regression method [21] learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena [22][23][24]. The description of the mapping method that combines the merits of unsupervised multiple learning techniques and combinations of regressions.…”

Section: Literature Surveymentioning

confidence: 99%

Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm

Lalitha¹,

Thyagharajan²

2019

IJCIS

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Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and center loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) in terms of accuracy and Mean Absolute Error (MAE).

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“…Most recently, Boucha®ra et al 24 introduced a novel formalism that performs dimensionality reduction and captures topological features (such as the shape of the observed data) to conduct pattern classi¯cation. This mission is achieved by: (1) Reducing the dimension of the observed variables through a kernelized radial basis function technique and expressing the latent variables probability distribution in terms of the observed variables; (2) disclosing the data manifold as a 3D polyhedron via the -shape constructor and extracting topological features and (3) classifying a data set using a mixture of multinomial distributions.…”

Section: Introductionmentioning

confidence: 99%

Age-Invariant Face Recognition Using Triangle Geometric Features

Ali

Asirvadam

Malik

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Whilst facial recognition systems are vulnerable to di®erent acquisition conditions, most notably lighting e®ects and pose variations, their particular level of sensitivity to facial aging e®ects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual statement estimated deterioration in the performance of face recognition systems due to facial aging. There was about 5% degradation in the accuracies of the face recognition systems for each single year age di®erence between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a signi¯cant requirement for building an e®ective facial recognition system. The main objective of this work is to address the challenge of facial aging which a®ects the performance of facial recognition systems. Accordingly, this work presents a geometrical model that is based on extracting a number of triangular facial features. The proposed model comprises a total of six triangular areas connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore, a set of thirty mathematical relationships are developed and used for building a feature vector for each sample image. The areas and perimeters of the extracted triangular areas are calculated and used as inputs for the developed mathematical relationships. The performance of the system is evaluated over the publicly available face and gesture recognition research network (FG-NET) face aging database. The performance of the system is compared with that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant face recognition systems. Our proposed system yielded a good performance in term of classi¯cation accuracy of more than 94%.

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Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Cited by 33 publications

References 26 publications

Age Invariant Face Recognition using Convolutional Neural Network

Age Invariant Face Recognition using Convolutional Neural Network

Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm

Age-Invariant Face Recognition Using Triangle Geometric Features

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