Proceedings ArticleDOI
Age Classification Using an Optimized CNN Architecture
M. Fatih Aydogdu,M. Fatih Demirci +1 more
- pp 233-239
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TLDR
An optimized Convolutional Neural Network architecture for the age classification problem is proposed and the CNN architecture involving 4 convolutional layers and 2 fully connected layers is found to be superior to the other CNN-based architectures with different number of layers.Abstract:
With growing data size in multimedia systems, the need for successful image classification and retrieval systems becomes vital. Nevertheless, the performance of such systems is still limited for real-world applications. In this paper, we propose an optimized Convolutional Neural Network (CNN) architecture for the age classification problem. In order to justify the structure and depth of the proposed CNN-based framework, comprehensive experiments on a number of different CNN architectures are conducted. Based on the fitness of the age classification results with respect to success-error ratios, training times, and standard deviations of success rates; using exact, top-3 and 1-off criterion, the CNN architecture involving 4 convolutional layers and 2 fully connected layers is found to be superior to the other CNN-based architectures with different number of layers. We evaluate our method on a face database consisting of more than 55,000 images.read more
Citations
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MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.
Karl Ricanek,Tamirat Tesafaye +1 more
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
Proceedings ArticleDOI
Cifar-10 Image Classification with Convolutional Neural Networks for Embedded Systems
TL;DR: The experimental analysis shows that 85.9% image classification accuracy is obtained by the framework while requiring 2GB memory only, making the framework ideal to be used in embedded systems.
Journal ArticleDOI
Deep learning approach for facial age classification: a survey of the state-of-the-art
TL;DR: A thorough study of the state-of-the-art deep learning techniques which estimate age from human faces, the popular convolutional neural network architectures used for age estimation, a critical analysis of the performance of some deep learning models on popular facial aging datasets, and the standard evaluation metrics used for performance evaluations are studied.
Journal ArticleDOI
Gender Classification and Age Estimation using Neural Networks: A Survey
Gangesh Trivedi,Nitin Pise +1 more
TL;DR: Various elements related to neural network model such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research.
Journal ArticleDOI
Facial Spots Detection Using Convolution Neural Network
TL;DR: The main aim is to detect a facial spots present in the face using the Convolution Neural Network which can detect a face under different lighting conditions very efficiently and is very helpful in different surgical processes on the face.
References
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Posted Content
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