6 research outputs found
Entropy-based Iterative Face Classification
Abstract. This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropybased measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem
Human Face Recognition Using Discriminant Analysis
In the present research, a face recognition method is proposed based on the concept of linear discriminant analysis (LDA) method. The LDA requires input some of image models to analyze and discriminate them, the newly proposed idea is the use of a number of textural features instead of face image pixels to be input the LDA procedure. The employed textural features were ten, which are computed for each face image using the grey level co-occurrence matrix (GLCM) method. The proposed face recognition method consists of two phases: enrollment and recognition. The enrollment phase is responsible for collecting the features of each face image to be a comparable models stored in the database, while the recognition phase is responsible on comparing the extracted features of input unknown face with that stored in the database. The comparison results a number of percentage values, each refers to the similarity between the input unknown face with the models in the database. The recognition decision is then issued according to the comparison results. The results showed that the system performed the recognition test with a recognition percent of about 94%, whereas the validation test showed that the system performance was about 92%. Frequent practices showed that the behavior of the recognition is acceptable and it is enjoying with the ability to be improved.
Brain inspired approach to computational face recognition
Face recognition that is invariant to pose and illumination is a problem solved effortlessly
by the human brain, but the computational details that underlie such efficient
recognition are still far from clear.
This thesis draws on research from psychology and neuroscience about face and object
recognition and the visual system in order to develop a novel computational
method for face detection, feature selection and representation, and memory structure
for recall.
A biologically plausible framework for developing a face recognition system will be
presented. This framework can be divided into four parts: 1) A face detection
system. This is an improved version of a biologically inspired feedforward neural
network that has modifiable connections and reflects the hierarchical and elastic
structure of the visual system. The face detection system can detect if a face is
present in an input image, and determine the region which contains that face. The
system is also capable of detecting the pose of the face. 2) A face region selection
mechanism. This mechanism is used to determine the Gabor-style features corresponding
to the detected face, i.e., the features from the region of interest. This
region of interest is selected using a feedback mechanism that connects the higher
level layer of the feedforward neural network where ultimately the face is detected to an intermediate level where the Gabor style features are detected. 3) A face recognition
system which is based on the binary encoding of the Gabor style features
selected to represent a face. Two alternative coding schemes are presented, using
2 and 4 bits to represent a winning orientation at each location. The effectiveness
of the Gabor-style features and the different coding schemes in discriminating faces
from different classes is evaluated using the Yale B Face Database. The results from
this evaluation show that this representation is close to other results on the same
database. 4) A theoretical approach for a memory system capable of memorising
sequences of poses. A basic network for memorisation and recall of sequences of
labels have been implemented, and from this it is extrapolated a memory model
that could use the ability of this model to memorise and recall sequences, to assist
in the recognition of faces by memorising sequences of poses.
Finally, the capabilities of the detection and recognition parts of the system are
demonstrated using a demo application that can learn and recognise faces from a
webcam