We propose a framework for face recognition performed in 3D space. A 3D facial model consisting of a sparse depth map is constructed from stereo images using isoluminance lines for the stereo matching. By searching for arcs whose radiuses are of certain ranges, we can locate the candidate irises very efficiently. After the pose of the face is detected, the 3D model is transformed into a canonical pose. Recognition is performed by calculating the mean differences in depth between the corresponding data points in the 3D test model and all the models in the database. The corresponding data points refer to a pair of closest data points in two 3D models. We show that even without using any 2D features, we can do face recognition depending on the depth information only. The 3D pose detecting algorithm makes it possible for our face recognition algorithm to be pose invariant.

3D template matching for pose invariant face recognition using 3D facial model built with isoluminance line based stereo vision

This paper describes a method to detect glasses frames for robust facial image processing. This method makes use of the 3D features obtained by a trinocular stereo vision system. The glasses frame detection is based on the fact that the rims of a pair of glasses lie on the same plane in 3D space. We use a 3D Hough transform to obtain a plane in which 3D features are concentrated. Then, based on the obtained 3D plane and with some geometry constraints, we can detect a group of 3D features belonging to the frame of the glasses. Using this approach, we can separate the 3D features of the glasses frame from those of facial features. This approach does not require any prior knowledge about face pose, eye positions, or the shape of the glasses.

/pdf/glasses-frame-detection-with-3d-hough-transform-hlfiupufzv.pdf

Glasses frame detection with 3D Hough transform

Automatic face recognition has a lot of application areas and current single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. A face recognition system using multiple cameras overcomes these limitations. In each channel, real-time component-based face detection detects the face with moderate pose and illumination changes employing fusion of individual component detectors for eyes and mouth, and the normalized face is recognized using an LDA recognizer. A reliability measure is trained using the features extracted from both face detection and recognition processes, to evaluate the inherent quality of channel recognition. The recognition from the most reliable channel is selected as the final recognition results. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules

/pdf/multi-camera-face-recognition-by-reliability-based-selection-5askzze4wx.pdf

Multi-Camera Face Recognition by Reliability-Based Selection

Single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. Face recognition using multiple synchronized cameras is proposed to overcome the limitations. We introduce a reliability measure trained from examples to evaluate the inherent quality of channel recognition. The recognition from the channel predicted to be the most reliable is selected as the final recognition results. In this paper, we enhance Adaboost to improve the component based face detector running in each channel as well as the channel reliability measure training. Effective features are designed to train the channel reliability measure using data from both face detection and recognition. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules

/pdf/on-channel-reliability-measure-training-for-multi-camera-5b4b5ajmo5.pdf

On Channel Reliability Measure Training for Multi-Camera Face Recognition

In this paper we present a 3D human face reconstruction framework based on stereo sensor coupled with a structured lighting source. Starting from two calibrated images, the active part (video projector) which project controlled lights, allows the operator to locate two sets of structured features with sub-pixel accuracy in both left and right images. Then, exploiting epipolar geometry improves the matching process by reducing its complexity from a bidirectional to a unidirectional search problem. Finally, we perform an adapted dynamic programming algorithm to obtain corresponding features in each conjugated scanline separately. Final three dimensional face models are achieved by a pipeline of four steps: (a) stereo triangulation, (b) data interpolation based on cubic spline models, (c) Delaunay triangulation-based meshing, and (d) texture mapping process.

/pdf/3d-face-modeling-based-on-structured-light-assisted-stereo-2uqgrcy4lw.pdf

3D face modeling based on structured-light assisted stereo sensor

A segment-based stereo vision system has been modified to reconstruct 3D facial models. Because usually it is difficult to get enough edges to reconstruct a 3D facial model with edge-based stereo algorithms, most research on 3D face model is based on correlation stereo algorithms, even though these algorithms are often time-consuming. We present a fast 3D face reconstruction algorithm using isoluminance lines from stereo images, which is effective even when only a few edges can be detected. We also introduced very efficient algorithm do search for irises in 3D space, which can then be used to identify the face position and direction. Our experiments show that our 3D facial model is suitable for 3D face recognition.

M. Kawade

Papers

Building 3D facial models and detecting face pose in 3D space