Interestingness Improvement of Face Images by Learning Visual Saliency

Connecting features of face images with the interestingness of a face may assist in a range of applications such as intelligent visual human-machine communication. To enable the connection, we use interestingness and image features in combination with machine learning techniques. In this paper, we u...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2020-09, Vol.24 (5), p.630-637
Main Author: Anh, Dao Nam
Format: Article
Language:English
Subjects:
Image classification
Machine learning
Object recognition
Salience
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Summary:Connecting features of face images with the interestingness of a face may assist in a range of applications such as intelligent visual human-machine communication. To enable the connection, we use interestingness and image features in combination with machine learning techniques. In this paper, we use visual saliency of face images as learning features to classify the interestingness of the images. Applying multiple saliency detection techniques specifically to objects in the images allows us to create a database of saliency-based features. Consistent estimation of facial interestingness and using multiple saliency methods contribute to estimate, and exclusively, to modify the interestingness of the image. To investigate interestingness – one of the personal characteristics in a face image, a large benchmark face database is tested using our method. Taken together, the method may advance prospects for further research incorporating other personal characteristics and visual attention related to face images.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2020.p0630