Title
Landmark-Guided Local Deep Neural Networks for Age and Gender Classification.
Abstract
Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a "global" network. The age and gender classification results are obtained by combining both the outputs from both the "global" and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.
Year
DOI
Venue
2018
10.1155/2018/5034684
JOURNAL OF SENSORS
Field
DocType
Volume
Computer vision,Pattern recognition,Artificial intelligence,Face detection,Biometrics,Engineering,Artificial neural network,Landmark,Deep neural networks
Journal
2018
ISSN
Citations 
PageRank 
1687-725X
1
0.39
References 
Authors
8
2
Name
Order
Citations
PageRank
Yungang Zhang18710.05
Tianwei Xu2195.29