Abstract
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.
Sign-in/Register to access full text options 
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 callMore From: Journal of Advanced Computational Intelligence and Intelligent Informatics
Disclaimer: 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.
