An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition

Abstract

Abstract-We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D Spherical Face Representation (SFR) is used in conjunction with the Scale-Invariant Feature Transform (SIFT) descriptor to form a rejection classifier, which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach, which is robust to facial expressions. This approach automatically segments the eyesforehead and the nose regions, which are relatively less sensitive to expressions and matches them separately using a modified Iterative Closest Point (ICP) algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms that used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74 percent and 98.31 percent verification rates at a 0.001 false acceptance rate (FAR) and identification rates of 99.02 percent and 95.37 percent for probes with a neutral and a nonneutral expression, respectively.

Robust binary neural networks based 3D Face detection and accurate face registration

“…The length of c is shorter than the length of a and b and the length of a is longer than the length of b. That fact has been illuminated by A. Mian et al 21 and L. Zhang et al 31 . Thereupon, according to the distribution information of points such as a,b and c, the top three largest principle components can be used as x, y and z coordinates axes.…”

“…Therefore, a face registration/alignment step is required. Mian et al 21 used a Principle Component Analysis (P-CA) based algorithm to correct the pose variations. Three principle components are used as the x, y and z-coordinates of the point cloud of a face.…”

“…1. PCA-based face alignment using the whole face area which is introduced in section 3.1 (a similar method is used in 21 ).…”

3D Face Recognition: Technology and Applications

“…Mian et al [62] extract inflection points around the nose tip and utilize these points for segmenting the face into eye-forehead and nose regions. The regions, that are less affected under expression variations, are separately matched with ICP and the similarity scores are fused at the metric level.…”

A Fast and Fully Automatic Ear Recognition Approach Based on 3D Local Surface Features

“…Since ICP is computationally expensive, we extracted a reduced rectangular region fed to a modified version of ICP in [19]. The minimum and maximum co-ordinate values of the matched local 3D features were used to extract the reduced rectangular region from the originally detected gallery and probe ear data.…”