[논문]인셉션 모듈 기반 컨볼루션 신경망을 이용한 얼굴 연령 예측

조현종

doi:10.5370/kiee.2018.67.9.1224

인셉션 모듈 기반 컨볼루션 신경망을 이용한 얼굴 연령 예측
Facial Age Estimation Using Convolutional Neural Networks Based on Inception Modules 원문보기

전기학회논문지 = The Transactions of the Korean Institute of Electrical Engineers, v.67 no.9, 2018년, pp.1224 - 1231

(Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University) , 조현종 (Dept. of Electronic Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University)

Abstract ▼ AI-Helper

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.

주제어

AI 본문요약
AI-Helper

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문제 정의

Many studies on age estimation have been conducted. In this paper, we strive to demonstrate similar advancements using a deep learning architecture that is designed by considering the limited availability of accurate age labels in existing face dataset. We test our network on the newly released Adience benchmark age estimation face images[3].
The Adience benchmark is one of the most recent dataset designed for age estimation from face images. In this study, the Adience dataset was used to evaluate the efficiency of the proposed method. It consists of the unconstrained face images of 2,284 subjects and has eight age groups.
This paper provides a methodology to estimate the real age groups of human by analyzing frontal face images. This process involves four stages: face detection, pre-processing, classification, and age estimation.

제안 방법

Experiments were conducted to evaluate the proposed method. Our proposed method includes “face detection,” “histogram equalization,” an “image sharpening filter,” and a “convolutional deep-learning-based inception module.
In this study, we used the Adience dataset for training and testing. Adience images attempt to resize all of the in-visage features, the lighting quality, the head pose, noise, and other aspects.
In this paper, we proposed a method to perform age estimation based on facial images by using a convolutional neural network based on an inception module. It was modified and fine-tuned to perform age estimation, and it was trained for face detection for a large dataset. The proposed method outperformed the previous approach for the Adience dataset by 4%, which is the newest challenging age estimation benchmark consisting of unconstrained facial images.
The one-off accuracy includes one adjacent age group, above or below. On the basis of our results, the proposed method significantly outperforms in terms of the exact accuracy and one-off accuracy. These results confirm the efficiency of the proposed work.
These results provide a remarkable baseline for deep-learning-based approaches. The proposed method can be improved with more data sets. Future work will focus on obtaining a higher accuracy for age estimation with large data sets and the development of a gender estimation architecture for the proposed method.
It was modified and fine-tuned to perform age estimation, and it was trained for face detection for a large dataset. The proposed method outperformed the previous approach for the Adience dataset by 4%, which is the newest challenging age estimation benchmark consisting of unconstrained facial images. In addition to facial age estimation, we tested underage and gender-based age estimation.
This process involves four stages: face detection, pre-processing, classification, and age estimation. The system uses two types of face images: training face images where age group is known and test face images where the age group is unknown. We show that our proposed method, applied to the more challenging Adience benchmark designed for the same task, outperforms other recent methods.

대상 데이터

As a consequence of the influence of makeup, it can be more difficult to estimate the real age of women. In k-fold cross-validation, 377 female images and 323 male images were used for the test. Further, Label 1 (0-2) was excluded in this experiment because it is very difficult to estimate the gender for this age group.
In this study, the Adience dataset was used to evaluate the efficiency of the proposed method. It consists of the unconstrained face images of 2,284 subjects and has eight age groups. We used 4,000 face images, and each age group comprised 500 images.
We used 4,000 facial images from the Adience dataset[1]. It includes 1,723 male, 1,777 female, and 500 baby facial images[1]. Each image is added with the person’s age range (eight possible ranges)[1].
Table 1 summarizes the details of the dataset used. The dataset was acquired from the Computer Vision Lab at the Open University of Israel(OUI)[1]. We selected images that were generally front-facing, i.
The training dataset used 3,200 images, with 400 in each of the eight age groups. The testing set comprised 800 images, and each age group had 100 images. The initial learning rate was 0.
We cleaned the dataset to remove images in which faces were not fully viewable. The training dataset used 3,200 images, with 400 in each of the eight age groups. The testing set comprised 800 images, and each age group had 100 images.
Our method was implemented using the Matlab (2017a) open- source framework. Training was performed using a GeForce GTX 750Ti with 640 CUDA cores and 8GB of video memory. Training each network required approximately 2 h, and predicting the age for a single image using our network required approximately 450ms.
It consists of the unconstrained face images of 2,284 subjects and has eight age groups. We used 4,000 face images, and each age group comprised 500 images. We cleaned the dataset to remove images in which faces were not fully viewable.

이론/모형

Face detection is the basis of the method described by Viola and Jones[13] using Haar-like features. The Viola and Jones face detection method has four steps[14]: selection of Haar-like features; creation of an integral image; AdaBoost training; and cascade classification.

후속연구

The proposed method can be improved with more data sets. Future work will focus on obtaining a higher accuracy for age estimation with large data sets and the development of a gender estimation architecture for the proposed method.
Labels 1-4 are in the underage group. This experiment might help to control illegal underage activities such as entering bars or wine shops and purchasing tobacco products. Fig.

참고문헌 (20)

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상세보기
E. Eidinger, R. Enbar, and T. Hassner, "Age and gender estimation of unfiltered faces", IEEE Transactions on Information Forensics and Security, vol. 9, 2014, pp. 2170-2179.

상세보기
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상세보기
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상세보기
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상세보기
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