Paper Info
Title
Face age classification based on a deep hybrid model.
Abstract
Face age estimation, a computer vision task facing numerous challenges due to its potential applications in identity authentication, human–computer interface, video retrieval and robot vision, has been attracting increasing attention. In recent years, the deep convolutional neural networks (DCNN) have achieved state-of-the-art performance in age classification of face images. We propose a deep hybrid framework for age classification by exploiting DCNN as the raw feature extractor along with several effective methods, including fine-tuning the DCNN into a fine-tuned deep age feature extraction (FDAFE) model, introducing a new method of feature extracting, applying the maximum joint probability classifier to age classification and a strategy to incorporate information from face images more effectively to improve estimation capabilities further. In addition, we pre-process the original image to represent age information more accurately. Based on the discriminative and compact framework, state-of-the-art performance on several face image data sets has been achieved in terms of classification accuracy.
Year
DOI
Venue
2018
10.1007/s11760-018-1309-6
Signal, Image and Video Processing
Keywords
Field
DocType
Deep convolutional neural networks (DCNN), Face age estimation, Fine-tuned deep age feature extraction (FDAFE) model, Maximum joint probability classifier (MJPC)
Computer vision,Data set,Joint probability distribution,Authentication,Pattern recognition,Convolutional neural network,Feature extraction,Artificial intelligence,Extractor,Classifier (linguistics),Discriminative model,Mathematics
Journal
Volume
Issue
ISSN
12
8
1863-1703
Citations 
PageRank 
References 
1
0.35
57
Authors
6
Name
Order
Citations
PageRank
Liming Chen12607201.71
Cien Fan221.03
Haiyan Yang342.79
Shiyong Hu411.02
Lian Zou5302.19
Dexiang Deng6694.43