Face Verification Across Age Progression Using Discriminative Methods

Ling, Haibin; Soatto, Stefano; Ramanathan, N.; Jacobs, David W.

doi:10.1109/tifs.2009.2038751

Cited by 153 publications

(13 citation statements)

References 42 publications

(71 reference statements)

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“…They measured drift coherency in both children and adults and presented promising preliminary results. Ling et al [11] used discriminant image descriptors based on Gradient Orientation Pyramids (GOP). They found that GOP representation combined with SVM classification is more invariant to aging effects compared to other methods, and that the difficulty involved in face verification saturates for age gaps between 4 years and 10 years.…”

Section: Discriminative Methodsmentioning

confidence: 99%

Face Recognition across Time Lapse Using Convolutional Neural Networks

Khiyari¹,

Wechsler²

2016

JIS

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Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning is found to produce highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the challenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including commercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH.

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Section: Discriminative Methodsmentioning

confidence: 99%

Face Recognition across Time Lapse Using Convolutional Neural Networks

Khiyari¹,

Wechsler²

2016

JIS

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“…To eliminate illumination, effect efficient preprocessing technique used in [16]. Gabor wavelet based feature extraction [17] is employed in effective face recognition but size of feature vector was obtained being large delays effective computation.…”

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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“…The left and right eye, left and right nose, mouth were the features. Ling et al (2010) have implemented a discriminative approach to capture the face features, Support Vector Machine (SVM) and Gradient Orientation Pyramid (GOP) to be applied for face recognition. The quality of photos, glass appearance, facial hair changes were taken as the features of age recognition.…”

Section: Jcsmentioning

confidence: 99%

“…Ichikawa et al (2008) have implemented the usage of decision tree C4.3 algorithm, to classify the occluded and non occluded part of the facial image. Ling et al (2010) have discussed a discriminative approach to fully capture the face features, Support Vector Machine (SVM) and Gradient Orientation Pyramid (GOP) to be applied for face Science Publications…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Based Occlusion Detection in Face Recognition and Estimation of Human Age Using Back Propagation Neural Network

Karthigayani¹,

Sridhar²

2014

Journal of Computer Science

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Occlusion detection in face verification is an essential problem that has not widely addressed. In this study the research is deals about occlusion detection in face recognition and estimation of human age using image processing. The objects hide from another object is called as occlusion. Occlusion conditions may vary from face wearing sunglasses, wearing of scarf in the eyes and mouth positions. The proposed work consists four stages. Initial stage is to extract the features using canny edge detection technique and to classify the occluded and non occluded region using Decision Tree Based Occlusion Detection (DTOD) classifier. Secondly the face verification and recognition is carried out using Elastic Matching Pattern (EMP) and Maximum Likelihood Classifier (MLC). Back Propagation Neural Network (BPNN) can be used to estimate the age of the human in the third stage. Our experiments are conducted on the database images for the first stage. By considering the first stage the various performance measures of the classifiers are analyzed. The correctly classified instances rate are high compared with the existing classifiers like random forest and bayes classifier. Experiments are conducted using ORL dataset for the second and the third stage. On the basis of the results obtained from the second stage we observed that the face verification was completed with 95% of accuracy. In the third stage, the age estimation using BPNN algorithm shows better performance results compared with the existing neural network algorithm.

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Face Verification Across Age Progression Using Discriminative Methods

Cited by 153 publications

References 42 publications

Face Recognition across Time Lapse Using Convolutional Neural Networks

Face Recognition across Time Lapse Using Convolutional Neural Networks

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

Decision Tree Based Occlusion Detection in Face Recognition and Estimation of Human Age Using Back Propagation Neural Network

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