Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

By Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson, Brian C. Lovell. CVPR 2011 WORKSHOPS, 2011

340 citations recorded by the source corpus.

In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4061c5d35d7a3af31c4bf00c17a8d1039058b598

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaoming Liu
2. Xi Yin
3. Luan Tran
4. Conrad Sanderson
5. Xiaoou Tang
6. Xiaogang Wang
7. Luc Van Gool
8. Yujun Shen

In Signal Processing

1. Xiaoming Liu
2. Xi Yin
3. Luan Tran
4. Conrad Sanderson
5. Xiaoou Tang
6. Yujun Shen
7. Yaojie Liu
8. Amin Jourabloo

Citations in the corpus

• Disentangled Representation Learning GAN for Pose-Invariant Face Recognition (2017, background): “Face quality prediction is a classic topic where many prior work attempt to estimate the former from the latter [27, 37].”
• Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments (2010, methodology): “The library has been successfully used for the development of several algorithms and systems, such as [12, 13, 14, 16, 20].”