Journal of Sensors

Volume 2016, Article ID 5491341, 9 pages

http://dx.doi.org/10.1155/2016/5491341

## Facial Feature Extraction Using Frequency Map Series in PCNN

College of Information, Yunnan University, Kunming 650091, China

Received 20 March 2015; Accepted 14 May 2015

Academic Editor: Gwanggil Jeon

Copyright © 2016 Rencan Nie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Pulse coupled neural network (PCNN) has been widely used in image processing. The 3D binary map series (BMS) generated by PCNN effectively describes image feature information such as edges and regional distribution, so BMS can be treated as the basis of extracting 1D oscillation time series (OTS) for an image. However, the traditional methods using BMS did not consider the correlation of the binary sequence in BMS and the space structure for every map. By further processing for BMS, a novel facial feature extraction method is proposed. Firstly, consider the correlation among maps in BMS; a method is put forward to transform BMS into frequency map series (FMS), and the method lessens the influence of noncontinuous feature regions in binary images on OTS-BMS. Then, by computing the 2D entropy for every map in FMS, the 3D FMS is transformed into 1D OTS (OTS-FMS), which has good geometry invariance for the facial image, and contains the space structure information of the image. Finally, by analyzing the OTS-FMS, the standard Euclidean distance is used to measure the distances for OTS-FMS. Experimental results verify the effectiveness of OTS-FMS in facial recognition, and it shows better recognition performance than other feature extraction methods.

#### 1. Introduction

Face recognition is an important research field of pattern recognition; it has good potential applications in biological recognition technology, security system, and so on. At present, there are many face images through sensors, so we need a good algorithm to deal with these images. In the process, because of face data space caused by the problem of dimension disaster, facial feature extraction method with spatial dimension reduction effect is becoming the key technology of face recognition. In the past several decades, many researchers proposed a lot of methods to extract facial features such as geometric characteristics, subspace analysis [1–5], and neural network method [6].

The method of geometric characteristics [7] uses the calculation of geometric parameters as the face features; it has good adaptability to illumination changes, but poor adaptability to the more obvious facial expression, posture, and rotation changes, and so forth. At present, the subspace analytical method is a popular face recognition method, it employs a transform method of linear or nonlinear to make the data in the cast shadow space embodying explicit feature pattern, so as to extract the key features such as the method of PCA [1], LDA [2], and ICA [3] based on linear transformation and the method of KPCA [4], KFDA [5], and KICA based on nonlinear transformation. However, these methods have poor adaptability to the changes of rotation and distortion in the image. Neural network is based on the nonlinear transform ability of the network structure and uses the learning of the training sample to get nonlinear transforming space of the data and then to obtain the facial features according to the nonlinear transforming space, for instance, using SOM neural network and fuzzy RBF neural network, and so forth. But neural network method will likely cause over-fitting in the learning process for the samples of empirical risk minimization principle. Besides, the subspace analysis method and the face feature extraction method of traditional neural network need face samples to learn; if the training samples are changed, the projection transformation space of the data also wants to change; thus, the calculated amount in large-scale human face feature extraction is too large; its application in the real-time demand higher occasion is limited.

In 1993, Johnson and Ritter proposed pulse coupled neural network (PCNN) [8] based on Eckhorn research in cat’s visual cortex. It has widely been used in image segmentation [9, 10], image fusion [11–13], image retrieval [14], and so forth. In PCNN, the similarity group neurons will issue synchronous pulses under the effect of mutual coupling pulses. These pulses information constitute a 3D binary map series (BMS), which effectively describes the information of the edge and regional distribution of the image. But the data size of the BMS is larger and cannot be directly used as the image features; for this reason, by calculating the area of the binary image, Johnson [15] transform BMS into 1D oscillation time sequences (OTS), and the OTS has a good invariance in geometric transforming such as rotation, translation, and zoom. Based on the pulse mechanism of the neuron in PCNN, the pulses are divided into the capture pulses and self-excitation pulses, and the OTS is divided into C-OTS and S-OTS, and serve as the facial features extraction in face recognition [16]. Reference [14] extracted 1D OTS of the BMS by calculating the normalization rotational inertia (NRI) of the binary image and applying it to the image retrieval. However, the OTS feature extraction method of the image based on BMS did not fully consider the correlation between BMS in the binary images; those discontinuous features regions will cause influence for the pattern classification capability of OTS features. In addition, the OTS of Johnson’s form is statistical characteristics in the sense of whole situation of the binary images; these did not consider the spatial structure of the image, and the spatial structure information often plays an important role in pattern classification.

In view of the above analysis, this paper proposed a novel face feature extraction method of OTS based on the BMS of PCNN output, and, compared with the traditional subspace analysis and neural network method, the results will not change with the sample space change.

#### 2. Pulse Coupled Neural Network

The PCNN model consists of the receptive field, the modulation field, and the pulse generator. In the receptive field, the neuron, respectively, receives the coupling pulse input and external stimulus input of neighboring neurons and consists of and channels, which is described by (1). In and channels of the neuron, the neuron links with its neighborhood neurons via the synaptic linking weights and , respectively; the two channels accumulate input and exponential decay changes at the same time; the decay exponentials of and channels are and , while the channel amplitudes are and :

In the modulation field, the linking input is added a constant positive bias; then, it is multiplied by the feeding input; the bias is unitary, is the linking strength, and the total internal activity is the result of modulation, which is described by

Pulse generator consists of a threshold adjuster, a comparison organ, and a pulse generator. Its function is to generate the pulse output , and it adjusts threshold value ; is threshold range coefficient, which is described by (3). When the internal state is larger than the threshold , the neuron generates a pulse, which is described by

In the above equations, the subscripts and denote the neuron location in a PCNN and denotes the current iteration (discrete time step), where varies from 1 to ( is the total number of iterations).

The PCNN used for image processing is a single layer two-dimensional array of laterally linked pulse coupled neurons as shown in Figure 1, and all neurons are identical. The number of neurons in the network is equal to the number of pixels in the input image. There exists one-to-one correspondence between the image pixels and network neurons, and the gray value of a pixel is taken as the external input stimulus of the neuron in channel; namely, . The output of each neuron results is two states, namely, pulse (1 state) and nonpulse (0 state), so the output states of neurons comprise a binary map.