Abstract
Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from frontal face images is becoming important, with various applications. Our proposed work is based on a binary classifier that only determines whether two input images are clustered in a similar class and trains a convolutional neural network (CNN) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based only on age data, but we found that the accumulated gender data can also be used to compare ages. Thus, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, using gender classification, our proposed architecture, which is trained with only age data, performs age comparison using the self-generated gender feature. The accuracy enhancement by multi-task learning, i.e. simultaneously considering age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method has better results than the state of the art in terms of age estimation on MegaAge-Asian and MORPH datasets.
Highlights
Machine learning-based age estimation from face images is becoming more and more important because it is widely used for individual authentication [1], forensic research [2], security control [3], human–computer interaction [3] and social media [4]
By applying a Siamese network-based deep metric learning for exact age estimation, we propose a method to converge the process of Siamese network learning
We used Inception V3 for convolutional neural network (CNN) model [13], which was pre-trained with ImageNet [14]
Summary
Machine learning-based age estimation from face images is becoming more and more important because it is widely used for individual authentication [1], forensic research [2], security control [3], human–computer interaction [3] and social media [4]. There have been many studies using deep learning based on CNNs [3], such as AlexNet [5], VggNet [6], and Inception [7], with wide use for image classification and image detection. AlexNet, VggNet, and Inception have recently been used for multi-class classification, and they are widely used as the base models of CNN. Deep expectation (DEX) [4] is an age estimation approach based on CNN models. It uses VggNet to resolve multi-class classification problems for age estimation and adopts a method to estimate the appropriate age through expectation value calculation, for which the trained results in the softmax layer
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
