Face recognition based on machine vision has achieved great advances and been widely used in the various fields. However, there are some challenges on the face recognition, such as facial pose, variations in illumination, and facial expression. So, this paper gives the recent advances in 3D face recognition. 3D face recognition approaches are categorized into four groups: minutiae approach, space transform approach, geometric features approach, model approach. Several typical approaches are compared in detail, including feature extraction, recognition algorithm, and the performance of the algorithm. Finally, this paper summarized the challenge existing in 3D face recognition and the future trend. This paper aims to help the researches majoring on face recognition.
A noise filtration technique for fabric defects image using curvelet transform domain Filters is proposed in this paper.
Firstly, we used FDCT_WARPING to decompose image into five scales curvelet coefficients. Secondly, the proposed
algorithm distinguished major edges from noise background at the third scale. Thirdly, the possible lost edges in the
procedure above were detected according to the decaying lever of the coefficients. Fourthly, the edges of the defect at the
second scale were detected by four correlation coefficients in the two directions at the third scale. Fifthly, the curvelet
coefficients at the fourth scale are filtered by the decaying lever. Sixthly, the curvelet coefficients at the fifth scale are
filtered by hard threshing. Finally, the processed coefficients are reconstructed. The tests on the developed algorithms
were performed with images from TILDA's Textile Texture Database, and suggest that the new approach outperforms
wavelet methods in image denoising.
Aiming at the change of battery location, environment light or camera location in Li/MnO2 automatic inspection process,
a novel WT-FEBFNN (Wavelet Transform Fuzzy Ellipsoidal Basis Function Neural Network) approach to battery defect
inspection is proposed. Firstly, WT is applied on original battery image, and low-frequency signal and de-noised signal is
obtained, respectively, by setting different thresholds on different scale WT decomposition. Secondly, signal only
containing defect (nick) is obtained by subtracting low-frequency signal from de-noised signal. Finally, model of
FEBFNN is established and defect recognition is accomplished on 1000 battery images. Experiments have shown the
proposed algorithm had a better robustness to the change of battery location, or environment light or camera location
than multilayer perception(MLP), and shown that the reason for the high recognition accuracy in battery defect
inspection is due to the information contents of the features as well as to proper classifier.
It is a challenging problem to overcome shift and rotation and nonlinearity in fingerprint images. By analyzing the
shortcoming of fingerprint recognition algorithm on shift or rotation images at present, manifold learning algorithm is
introduced. A fingerprint recognition algorithm has been proposed based on locally linear embedding of variable
neighbourhood k (VK-LLE). Firstly, approximate geodesic distance between any two points is computed by ISOMAP (
isometric feature mapping) and then the neighborhood is determined for each point by the relationship between its local
estimated geodesic distance matrix and local Euclidean distance matrix. Secondly, the dimension of fingerprint image is
reduced by nonlinear dimension-reduction method. And the best projected features of original fingerprint data of large
dimension are acquired. By analyzing the changes of recognition accuracy with the neighborhood and embedding
dimension, the neighborhood and embedding dimension is determined at last. Finally, fingerprint recognition is
accomplished by Euclidean distance Classifier. The experimental results based on standard fingerprint datasets have
verified the proposed algorithm had a better robustness to those fingerprint images of shift or rotation or nonlinearity
than the algorithm using LLE, thus this method has some values in practice.