Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions

In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sadhana (Bangalore) Jg. 41; H. 12; S. 1393 - 1406
Hauptverfasser: Susan, Seba, Aggarwal, Nandini, Chand, Shefali, Gupta, Ayush
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New Delhi Springer India 01.12.2016
Springer Nature B.V
Schlagworte:
Emotions
Engineering
Entropy
Forehead
Image coding
Information theory
Maximum entropy
Partitioning
Widening
Variable length coding
entropy coding
Shannon–Fano coding
maximum entropy partitioning
Huffman coding
facial expression recognition
non-extensive entropy with Gaussian gain
ISSN:0256-2499, 0973-7677
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight the set of low probability intensities that characterize the facial expression such as the creases in the forehead, the widening of the eyes and the opening and closing of the mouth. A new coding scheme based on maximum entropy partitioning is proposed in our work, particularly to identify the improbable intensities related to different emotions. The improbable intensities when used as a mask decode the facial expression correctly, providing an effective platform for future emotion categorization experiments.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0256-2499
0973-7677
DOI:10.1007/s12046-016-0559-7