Age Estimation of Face Images Based on CNN and Divide-and-Rule Strategy
TL;DR: This paper leverages excellent characteristics of convolution neural network in the field of image application, by using deep learning method to extract face features, and adopts factor analysis model to extract robust features.
Abstract: In recent years, the research on age estimation based on face images has drawn more and more attention, which includes two processes: feature extraction and estimation function learning. In the aspect of face feature extraction, this paper leverages excellent characteristics of convolution neural network in the field of image application, by using deep learning method to extract face features, and adopts factor analysis model to extract robust features. In terms of age estimation function learning, age-based and sequential study of rank-based age estimation learning methods is utilized and then a divide-and-rule face age estimator is proposed. Experiments in FG-NET, MORPH Album 2, and IMDB-WIKI show that the feature extraction method is more robust than traditional age feature extraction method and the performance of divide-and-rule estimator is superior to classical SVM and SVR.
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01 Jan 2006
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.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.
139 citations
TL;DR: In this paper, an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography was presented.
Abstract: Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.
29 citations
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.
Abstract: Age estimation using face images is an exciting and challenging task. The traits from the face images are used to determine age, gender, ethnic background, and emotion of people. Among this set of traits, age estimation can be valuable in several potential real-time applications. The traditional hand-crafted methods relied-on for age estimation, cannot correctly estimate the age. The availability of huge datasets for training and an increase in computational power has made deep learning with convolutional neural network a better method for age estimation; convolutional neural network will learn discriminative feature descriptors directly from image pixels. Several convolutional neural net work approaches have been proposed by many of the researchers, and these have made a significant impact on the results and performances of age estimation systems. In this paper, we present a thorough study of the state-of-the-art deep learning techniques which estimate age from human faces. We discuss the popular convolutional neural network architectures used for age estimation, presents a critical analysis of the performance of some deep learning models on popular facial aging datasets, and study the standard evaluation metrics used for performance evaluations. Finally, we try to analyze the main aspects that can increase the performance of the age estimation system in future.
29 citations
Cites methods from "Age Estimation of Face Images Based..."
...It has the ability to learn discriminative trait descriptors directly from image pixels (Liu et al. 2017a). These traits are needed to correctly estimate the age of people. AlexNet, GoogLeNet, VGGNet, ResNet, SqueezeNet and Xception CNN architecture are generally considered as the most common architectures because of their state-of-the-art performance on different benchmarks including age estimation task. The following are the description of the architectures: 2.1 AlexNet architecture One of the earliest CNN architecture for age estimation has been presented by Krizhevsky et al. (2017). AlexNet the winner of the 2012 edition of ImageNet Large Scale Visual Recognition Challenge (ILSVRC), was recorded as the first successful CNN architecture, trained on “imageNet” dataset with about 1....
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TL;DR: The experimental results demonstrate that the proposed approach for age estimation based on demographic classification can offer better performance when compared to the state-of-the-art methods on MORPH-II, PAL and a subset of LFW databases.
16 citations
TL;DR: This paper attempts to estimate age and gender from a single face real-time image using CNN, using Haar Cascades and Caffenet for face and gender recognition.
15 citations
References
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07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
5,563 citations
Journal Article•
TL;DR: A semi-automatical way to collect face images from Internet is proposed and a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace is built, based on which a 11-layer CNN is used to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF.
Abstract: Pushing by big data and deep convolutional neural network (CNN), the
performance of face recognition is becoming comparable to human. Using private large
scale training datasets, several groups achieve very high performance on LFW, ie, 97% to
99%. While there are many open source implementations of CNN, none of large scale face
dataset is publicly available. The current situation in the field of face recognition is that data
is more important than algorithm. To solve this problem, this paper proposes a semi-
automatical way to collect face images from Internet and builds a large scale dataset
containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on
the database, we use a 11-layer CNN to learn discriminative representation and obtain state-
of-theart accuracy on LFW and YTF.
1,705 citations
"Age Estimation of Face Images Based..." refers methods in this paper
...Stage 1: we firstly employ the large-scale face identities database CASIA-WebFace [30] to pretrain the deep network, which is much better than random initialization....
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...Therefore, we use the large-scale face database CASIA-WebFace [32] to pretrain the network....
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10 Apr 2006
TL;DR: The MORPH dataset as discussed by the authors is a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.
1,051 citations
07 Jun 2015
TL;DR: This paper proposes a simple convolutional net architecture that can be used even when the amount of learning data is limited and shows that by learning representations through the use of deep-convolutional neural networks, a significant increase in performance can be obtained on these tasks.
Abstract: Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.
1,046 citations
"Age Estimation of Face Images Based..." refers methods in this paper
...In [27], a six-layer CNN was used for gender and age grouping, but it did not address the problem of age estimation....
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