Journal of Visual Communication and Image Representation
Short CommunicationA gender classification system robust to occlusion using Gabor features based (2D)2PCA
Introduction
Face is a characteristic of human beings which uniquely reveals their identity, age and emotions. Gender classification from a person’s face can play an important role in a variety of computer vision based applications. Surveillance and security system design to find missing persons or criminals, biometric authentication systems such as smart systems controlling the access of people to prohibited areas, and search engines with an image filter to optimize the search are some of the potential applications. Gender classification, if used in conjunction with face recognition, makes the face recognition task twice as fast by eliminating the search for a particular gender.
Face biometrics is different from all other types of biometrics in the way face images are captured. In surveillance based security systems, a person does not volunteer himself/herself for scanning. Cameras placed in public areas usually examine thousands of faces for identification and therefore captured face images may not be perfect frontal images for examination. The probability of capturing occluded face images is very high either due to unintentional reasons (e.g. wearing sunglasses or scarf), or may be overlapping with the objects present in the environment. Also, a person may hide his face intentionally if he is aware that he is under surveillance.
In real life situations, it might be desirable to recognize gender from a face which may be partially occluded due to injury or some other reasons. In such situations a system is sought after, that is accustomed to gender recognition from occluded faces with a good classification rate. The objectives of this work are:
- 1.
To develop an efficient gender classification system not only for non-occluded face images but also for occluded face images.
- 2.
To enhance the classification accuracy and speed through an efficient and small sized feature vector.
- 3.
To develop a gender classification system robust against varying extents of occlusion for occluded face images.
For non-occluded face images, the proposed work gives acceptable classification rate of more than 95% on most of the face databases except on a large database collected in unconstrained environment. The performance of the system is discussed for face images occluded up to 60%.
Contents are organized as follows; Section 2 explores work done so far in gender classification; Section 3 describes the proposed gender classification system; experiments and performance analysis are covered in Section 4; and finally, Section 5 concludes the work along with the future scope.
Section snippets
Related work
Most of the researchers working on gender classification have focused on the application of a variety of feature extraction and classification techniques on full frontal face images. A number of works exist in literature targeting gender classification on occlusion free face images. Golomb et al. [1] and Tamura et al. [2] used neural network to classify gender from low resolution images. Later on, a Support Vector Machine (SVM) based method [3] was used to identify gender from low resolution
Proposed gender classification system
The proposed system starts with extraction of a Region of Interest (ROI) containing the most relevant information of face from the face image. To address the occlusion problem, local features from the face are extracted in addition to the global information of the face. It is observed that local features in face images are most robust to distortions caused by expression and illumination changes. To target these local properties, the Region of Interest (ROI) of a face image is partitioned into
Experiments and performance analysis
The proposed technique is exhaustively tested on five face databases for non-occluded as well as occluded face images. Analysis of experimental results gives some important clues about the relative importance of various face parts (eyes, nose, lips, etc.) for successful gender recognition.
Conclusion
A gender classification approach exploiting the potential of PCA and Gabor filter is presented in this work. Subdivision of a face image into varying sized blocks improves the performance of the system as compared to the approaches where gender classification directly targets full face image. The assertion is empirically verified by evaluating technique on standard face databases. Performance of the proposed system also owes to preliminary AFSI extraction which in a true sense assimilates
References (37)
- et al.
Male/female identification from 8 6 very low resolution face images by neural network
Pattern Recognit.
(1996) Learning local binary patterns for gender classification on real-world face images
Pattern Recognit. Lett.
(2012)- et al.
A support vector machine classifier with automatic confidence and its application to gender classification
Neurocomputing
(2011) - et al.
An experimental comparison of gender classification methods
Pattern Recognit. Lett.
(2008) Gender recognition: a multiscale decision fusion approach
Pattern Recognit. Lett.
(2010)- et al.
Facial gender classification using shape from shading
Image Vision Comput.
(2010) Robust gender classification using a precise patch histogram
Pattern Recognit.
(2013)- et al.
Gender classification by combining clothing, hair and facial component classifiers
Neurocomputing
(2012) - et al.
The feret database and evaluation procedure for face-recognition algorithms
Image Vision Comput.
(1998) - et al.
A new ranking method for principal components analysis and its application to face image analysis
Image Vision Comput.
(2010)