Facial Based Human Age Estimation Using Deep Belief Network

Shejul, Anjali A.; Kinage, Kishor S.; Reddy, B. Eswara

doi:10.1007/978-3-030-19562-5_27

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…In Figure .6 the FG-NET age distribution by year is detailed. We divide the FG-Net dataset to seven data subsets suitable for each kind of basics decades: [0-9] years, [10][11][12][13][14][15][16][17][18][19] years …and soon. Table 1 shown the percentage of each decade age group.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In [18] adopt a face image age estimation based on data augmentation and lightweight convolutional neural network. In [19] the authors use a beep belief network in their facial human age estimation. In [20] the authors use a specific domain transfer learning in age estimation.…”

Section: Related Workmentioning

confidence: 99%

“…They test the technique on IMDB-WIKI and FG-NET datasets. Anjali et al [19] use active appearance and scattering transform feature extraction method which allows extract geometric and texture features, in the classification step of their facial based human age estimation they use also the deep belief network classification model. To estimate the real facial age.…”

Section: Related Workmentioning

confidence: 99%

“…In offline process of the facial age group estimation and classification System, we grouped the dataset of facial photos into seven data subsets based on the value of age group that it represents:[0-9] for the first decade, [10][11][12][13][14][15][16][17][18][19] for the second decade, [20][21][22][23][24][25][26][27][28][29] 5 summarize and shown the twenty-eight referential faces per averages per age group decade. The second column contained the seven referential faces generated by arithmetic average; they are in orange color.…”

Section: Approachmentioning

confidence: 99%

“…We use age estimation and classification in our face sketch recognition system [11] and in face emotion classification [12], in this scenario it can be used to identify; fastly; a criminal person from his sketch for purposes of identification. They are many basics kinds of age groups but the famous are those they divided age by decades ([0-9], [10][11][12][13][14][15][16][17][18][19], ... and soon), for example, in the national ID card case. People's face is one of the most nonverbal exposed part of body.…”

Section: Introductionmentioning

confidence: 99%

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Human Face (Sketch/Photo) Age Group Estimation and Classification Using Perfect Face Ratios and Levenshtein Distance

Ounachad¹

2020

IJETER

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The main objective of this paper is to identify the age group of the human being based on their face images by using a geometric feature and Levenshtein distance. This paper presents a novel method for human face age group estimation and classification. It is inspired in our model which was pre-trained on face sketch gender classification and recognition task. Toward this end, we generate some referential faces suitable for each age group decades based on perfect face ratios and four classical averages. sixteen features will be extract from the input face image based on perfect face ratio rules. The detection stage passes by Viola and Jones algorithm. The classification task is evaluated through calculate of the Levenshtein distance between the input face image request and the referential faces. An experimental evaluation demonstrates the satisfactory performance of our approach on FG-NET database with 80.00 % for training, 20.00 % for the testing and divided by age groups decades. Also, the CUHK Face Sketch dataset (CUFS) is used for testing. The proposed algorithm will be a competitor of the proposed relative the stat of the art approaches. The estimate rate reaches more than 79.90 % for some age groups decade.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Face (Sketch/Photo) Age Group Estimation and Classification Using Perfect Face Ratios and Levenshtein Distance

Ounachad¹

2020

IJETER

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A decision system for computational authors profiling: From machine learning to deep learning

Mechti

Krichen

Noureddine

et al. 2020

Concurrency and Computation

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In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context-specific decision-making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state-of-the-art methods.

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Priority-based electric vehicle charge scheduling with multi-objective function using chimp tangent search algorithm

Ramya,

Shobana,

Suresh

et al. 2024

Electr Eng

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Facial Based Human Age Estimation Using Deep Belief Network

Cited by 4 publications

References 14 publications

Human Face (Sketch/Photo) Age Group Estimation and Classification Using Perfect Face Ratios and Levenshtein Distance

Human Face (Sketch/Photo) Age Group Estimation and Classification Using Perfect Face Ratios and Levenshtein Distance

A decision system for computational authors profiling: From machine learning to deep learning

Priority-based electric vehicle charge scheduling with multi-objective function using chimp tangent search algorithm

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