Partial Face Recognition System

Ms. Jayashree M. Dhikale, Prof. Nitin. M. Shahane


A number of face recognition methods are used
for identification or verification of a person in many computer
applications. Most of the methods use images of entire face
under controlled condition for face recognition. However, in many
situations the human faces might be occluded by other object
which poses a difficulty in face recognition. To overcome this
problem, the proposed approach identifies the persons. From
partial face, the proposed system tries matching it with the full
faces available in the database. It first extracts local features that
are textural features and geometrical features and also detect
keypoints. Then a Robust Point Set Matching (RPSM) method is
used to match local features sets and also keypoints of the faces
from database and the partial face image under investigation. A
distance metric is used to find similarity of the two faces using
their two sets of features.

Full Text:



C. Geng and X. Jiang, ”Face recognition based on the multi-scale local

image structures,” Pattern Recognit., vol. 44, nos. 10-11, pp. 2565-2575,

Oct./Nov. 2011.

X. Cao, Y. Wei, F. Wen, and J. Sun, ”Face alignment by explicit shape

regression,” in Proc. CVPR, Jun. 2012, pp. 2887-2894.

X. Zhu and D. Ramanan, ”Face detection, pose estimation, and landmark

localization in the wild,” in Proc. CVPR, Jun. 2012, pp. 2879-2886.

X. Xiong and F. de la Torre, ”Supervised descent method and its

applications to face alignment,” in Proc. CVPR, Jun. 2013, pp. 532-539.

J. Lu, Y.-P. Tan, and G. Wang, ”Discriminative multimanifold analysis for

face recognition from a single training sample per person,” IEEE Trans.

Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 39-51, Jan. 2013.

R. Weng, J. Lu, J. Hu, G. Yang, and Y.-P. Tan, ”Robust feature set

matching for partial face recognition,” in Proc. ICCV, Dec. 2013,pp. 601-

R. Weng,J. Lu, ”Robust Point Set Matching for Partial Face Recognition”

in IEEE Trans. Image Proc., Vol. 25, No. 3, March 2016, pp. 1163-1175.

G.Levi, T. Hassener, ”LATCH: Learned Arrangements of three patch”

Z. Li, G. Liu, Y. Yang, and J. You, ”Scale-and rotation-invariant local

binary pattern using scale-adaptive texton and subuniform-based circular

shift,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 2130-2140, Apr.

D. G. Lowe, ”Distinctive image features from scale-invariant keypoints,”

Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.


  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology