TY - GEN
T1 - Assessment of blurring and facial expression effects on facial image recognition
AU - Abdel-Mottaleb, Mohamed
AU - Mahoor, Mohammad H.
PY - 2006/6/15
Y1 - 2006/6/15
N2 - In this paper we present methods for assessing the quality of facial images, degraded by blurring and facial expressions, for recognition. To assess the blurring effect, we measure the level of blurriness in the facial images by statistical analysis in the Fourier domain. Based on this analysis, a function is proposed to predict the performance of face recognition on blurred images. To assess facial images with expressions, we use Gaussian Mixture Models (GMMs) to represent images that can be recognized with the Eigenface method, we refer to these images as "Good Quality", and images that cannot be recognized, we refer to these images as "Poor Quality". During testing, we classify a given image into one of the two classes. We use the FERET and Cohn-Kanade facial image databases to evaluate our algorithms for image quality assessment. The experimental results demonstrate that the prediction function for assessing the quality of blurred facial images is successful. In addition, our experiments show that our approach for assessing facial images with expressions is successful in predicting whether an image has a good quality or poor quality for recognition. Although the experiments in this paper are based on the Eigenface technique, the assessment methods can be extended to other face recognition algorithms.
AB - In this paper we present methods for assessing the quality of facial images, degraded by blurring and facial expressions, for recognition. To assess the blurring effect, we measure the level of blurriness in the facial images by statistical analysis in the Fourier domain. Based on this analysis, a function is proposed to predict the performance of face recognition on blurred images. To assess facial images with expressions, we use Gaussian Mixture Models (GMMs) to represent images that can be recognized with the Eigenface method, we refer to these images as "Good Quality", and images that cannot be recognized, we refer to these images as "Poor Quality". During testing, we classify a given image into one of the two classes. We use the FERET and Cohn-Kanade facial image databases to evaluate our algorithms for image quality assessment. The experimental results demonstrate that the prediction function for assessing the quality of blurred facial images is successful. In addition, our experiments show that our approach for assessing facial images with expressions is successful in predicting whether an image has a good quality or poor quality for recognition. Although the experiments in this paper are based on the Eigenface technique, the assessment methods can be extended to other face recognition algorithms.
KW - Blurring Effect
KW - Face recognition
KW - Facial expressions
KW - Gaussian Mixture Model
KW - Image Quality Assessment
UR - http://www.scopus.com/inward/record.url?scp=33744964572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33744964572&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33744964572
SN - 3540311114
SN - 9783540311119
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 12
EP - 18
BT - Advances in Biometrics - International Conference, ICB 2006, Proceedings
T2 - International Conference on Biometrics, ICB 2006
Y2 - 5 January 2006 through 7 January 2006
ER -