Facial expression recognition using a combination of multiple facial features and support vector machine.
The candidate pre-matching sets are first selected for each feature points based on the previously proposed algorithm called relative angle --context ...
To address the problem, in this paper, we propose a feature point localization algorithm based on the combination of. RACD and Support Vector Machine (SVM) on ...
This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on support vector machines and ...
In this paper, we propose an accurate facial feature point extraction algorithm that combines the good generalization ability of support vector machine (SVM) as ...
The goal of this study is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background)
Hongjing Ma, Detong Zhang, Jun Feng: Three-dimensional facial feature points matching based on a combined support vector machine. ICIMCS 2009: 139-144.
Classification was carried out by calculating the similarity score between the feature vectors. The SVM classifier was used in choosing the closest match.
Missing: machine. | Show results with:machine.
Mar 2, 2021 · First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, ...
Missing: dimensional points
This paper presents a feature-based face recognition system based on both 3D range data as well as 2D gray-level facial images.