Face Recognition on Linear Motion-blurred Image

Most face recognition algorithms are generally capable to achieve a high level of accuracy when the image is acquired under wellcontrolled conditions. The face should be still during the acquisition process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand still during the process is impractical; extremely likely that recognition should be performed on a blurred image. It is important to understand the relation between the image blur and the recognition accuracy. The ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty different persons, ten images per person. Those images were randomly divided into training and testing datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in the testing datasets were artificially blurred to represent a linear motion, and recognition was performed. The blurred images were also filtered using various methods. The accuracy levels of the recognition on the basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that at its best, the image improvement processes are capable to improve the recognition accuracy level by less than five percent

Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces