Representation and pre-processing techniques for illumination invariant 3D face recognition

Thakare, Nita; Thakare, V. M.

doi:10.1145/1980022.1980346

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally most of the applications use the 2D face recognition systems, but the performance of these systems badly affects due to changes in various factors like pose, expression, lighting conditions, etc. Nowadays 3D face recognition has become an ever-growing and promising biometric technique because of its robust features [9]. The 3D facial data can provide a promising way to understand the characteristics of the human face in 3D domain, and has potential possibility to improve the performance of the recognition system.…”

Section: D Face Databasementioning

confidence: 99%

A Supervised Hybrid Methodology for Pose and Illumination Invariant 3D Face Recognition

Thakare¹,

Thakare²

2012

IJCA

View full text Add to dashboard Cite

The 2D face recognition systems encounter difficulties in recognizing faces with illumination variations. The depth map of the 3D face data has the potential to handle the variation in illumination of face images. The view variations are handled by using the moment invariants. Moment Invariants are used as rotation invariant features of the face image. For feature matching an efficient fuzzy-neural technique is proposed. The PCA components of normalized depth map and Moment invariants on mesh images are used successfully to implement a fuzzy neural network based fully automatic 3D face recognition system. The system is evaluated on the 3D face databases, the CASIA database. The proposed system provides recognition accuracy that is resulted in to an efficient 3D face recognition system.

show abstract

Section: D Face Databasementioning

confidence: 99%

A Supervised Hybrid Methodology for Pose and Illumination Invariant 3D Face Recognition

Thakare¹,

Thakare²

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…Generally most of the applications use the 2D face recognition systems, but these systems are prone to various factors like pose, expression, lighting conditions, etc. Nowadays 3D face recognition has become a ever-growing and promising biometric technique because of its robust features [9]. The 3D facial data can provide a promising way to understand the characteristics of the human face in 3D domain, and has potential possibility to improve the performance of the recognition system.…”

Section: D Face Databasementioning

confidence: 99%

An innovative hybrid approach to construct fuzzy-neural network for 3D face recognition system

Thakare

2011

2011 11th International Conference on Hybrid Intelligent Systems (HIS)

Self Cite

View full text Add to dashboard Cite

The 2D face recognition systems encounter difficulties in recognizing faces with illumination variations. The depth map of the 3D face data has the potential to handle the variation in illumination of face images. For feature matching an efficient fuzzy-neural technique is proposed. This paper presents a new approach in which the depth maps of the 3D face images, containing the depth information of the face image are used. Since the input images contain the depth information the input to the fuzzy neural network is illumination invariant. Using the normalized depth map and fuzzy-neural network, a fully automatic 3D face recognition system is developed. The system is evaluated on the 3D face databases; the CASIA database. The proposed system efficiently handles the varying lighting effects and provides significant recognition accuracy.

show abstract

Representation and pre-processing techniques for illumination invariant 3D face recognition

Cited by 2 publications

References 0 publications

A Supervised Hybrid Methodology for Pose and Illumination Invariant 3D Face Recognition

A Supervised Hybrid Methodology for Pose and Illumination Invariant 3D Face Recognition

An innovative hybrid approach to construct fuzzy-neural network for 3D face recognition system

Contact Info

Product

Resources

About