erformance Analysis on Learning Algorithms with various Facial Expressions in Spiking Neural Networks 

Abstract

This paper is based on different expression classification and facial action recognition performed using human face database. This work aims to classify different facial expressions of the individuals from the static facial images from the JAFFE database with an improved spike model which is trained with the certain learning algorithms to recognize, the kind of expression with different poses such as happy, sad, neutral, fear and neutral.. This work challenges towards the development of more accurate and automated facial expression recognition methods of expressions. 

Authors and Affiliations

J. Lincy Kiruba , A. Diana Andrushia

Keywords

Related Articles

Clustering Among Multi-Posture Based Correspondence Compute 

All clustering models have to presuppose several cluster liaisons among the information substance that they are practical on. Comparison connecting a brace of substance can be distinct moreover unequivocally or unr...

Challenges in the Cloud Application Development 

Anyone who develops and tests software is well-known with the Software Development Life Cycle (SDLC). By its very nature, cloud-based development offers an organization a high degree of agility; correspondingly, th...

Combating Sybil Attacks using SybilGuard 

This paper presents a Sybil Guard, for combating against Sybil attacks without relying on a trusted central authority. Peer-to-peer and other decentralized, distributed systems are known to be particularly open to...

Image segmentation based on kernel fuzzy C means clustering using edge detection method on noisy images 

Classical fuzzy C-means (FCM) clustering is performed in the input space, given the desired number of clusters. Although it has proven effective for spherical data, it fails when the data structure of input pattern...

A Survey on Data Aggregation Techniques for Wireless Sensor Networks

Abstract-Wireless Sensor Network is an area of growing interest in which recent advancements in the field of sensing, computing and communication attracted various research efforts. Limitations of sensors involve power c...

Download PDF file
  • EP ID EP151723
  • DOI -
  • Views 95
  • Downloads 0

How To Cite

J. Lincy Kiruba, A. Diana Andrushia (2013). erformance Analysis on Learning Algorithms with various Facial Expressions in Spiking Neural Networks . International Journal of Advanced Research in Computer Engineering & Technology(IJARCET), 2(3), 1225-1228. https://europub.co.uk/articles/-A-151723