Face recognition/detection by probabilistic decision-based neural network

A face recognition software framework based on principal component analysis

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

Facial-recognition algorithms: A literature review

The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.

A Dataset for Comparing Mirrored and Non-Mirrored Male Bust Images for Facial Recognition

Facial recognition, as well as other types of human recognition, have found uses in identification, security, and learning about behavior, among other uses. Because of the high cost of data collection for training purposes, logistical challenges and other impediments, mirroring images has frequently been used to increase the size of data sets. However, while these larger data sets have shown to be beneficial, their comparative level of benefit to the data collection of similar data has not been assessed. This paper presented a data set collected and prepared for this and related research purposes. The data set included both non-occluded and occluded data for mirroring assessment.

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

Pixel-level alignment of facial images for high accuracy recognition using ensemble of patches

The variation of pose, illumination, and expression continues to make face recognition a challenging problem. As a pre-processing step in holistic approaches, faces are usually aligned by eyes. The proposed method tries to perform a pixel alignment rather than eye alignment by mapping the geometry of faces to a reference face while keeping their own textures. The proposed geometry alignment not only creates a meaningful correspondence among every pixel of all faces, but also removes expression and pose variations effectively. The geometry alignment is performed pixel-wise, i.e., every pixel of the face is corresponded to a pixel of the reference face. In the proposed method, the information of intensity and geometry of faces is separated properly, trained by separate classifiers, and finally fused together to recognize human faces. Experimental results show a great improvement using the proposed method in comparison to eye-aligned recognition. For instance, at the false acceptance rate (FAR) of 0.001, the recognition rates are respectively improved by 24% and 33% in the Yale and AT&T datasets. In the labeled faces in the wild dataset, which is a challenging, big dataset, improvement is 20% at a FAR of 0.1.

Stability and synchronization for discrete-time complex-valued neural networks with time-varying delays

In this paper, the synchronization problem for a class of discrete-time complex-valued neural networks with time-varying delays is investigated. Compared with the previous work, the time delay and parameters are assumed to be time-varying. By separating the real part and imaginary part, the discrete-time model of complex-valued neural networks is derived. Moreover, by using the complex-valued Lyapunov-Krasovskii functional method and linear matrix inequality as tools, sufficient conditions of the synchronization stability are obtained. In numerical simulation, examples are presented to show the effectiveness of our method.

Reliable Face Recognition Using Artificial Neural Network

Facial detection and recognition are among the most heavily researched fields of computer vision and image processing. Most of the current face recognition techniques suffer when the noises affect the global features or the local intensity pixels of the images under consideration. In the proposed Reliable Face Recognition System (RFRS) system, for the first time, a combination of Gabor Filter (GF), Principal component analysis (PCA) for efficient feature extraction, and ANN for classification is employed. This demonstrates how to detect faces in noisy images by training the network several times on various input; ideal and noisy images of faces. Applying GF before PCA reduces PCA sensitivity to noise, provides a greater level of invariance, and trains the ANN on different sets of noisy images. The output of the ANN is a vector whose length equal to the distinct subjects in Olivetti Research Laboratory (ORL). The ANN is trained to output a 1 in the correct position of the output vector and to fill the rest of the output vector with 0’s. Experimentation is carried out on RFRS by using ORL datasets. The experimental results show that training the network on noisy input images of face greatly reduce its errors when it had to classify or recognize noisy images. For noisy face images, the network did not make any errors for faces with noise of mean 0.00 or 0.05, while the average recognition rate varies from 96.8% to 98%. When noise of mean 0.10 is added to the images the network begins to make errors. For noiseless face images, the proposed system achieves correct classification. Performance comparison between RFRS and other face recognition techniques shows that for most of the cases, RFRS performs better than other conventional techniques under different types of noises and it shows the high robustness of the proposed algorithm.

COMBINED CLASSIFICATION OF MULTIPLE VIEWS USING FACIAL CORNERS

The profile view of a face provides a complementary structure that is not seen in the frontal view. The classification system combining both frontal and profile views of faces can improve the classification accuracy. And it would be more foolproof because it is difficult to fool the profile face identification by a mask. This paper proposes a new face recognition approach, which can be applied on both frontal and profile faces, to build a robust combined multiple view face identification system. The recognition employs a novel facial corner coding and matching method, and integrates the outline and interior facial parts in the profile matching. The proposed multiview modified Hausdorff distance fuses multiple views of faces to achieve an improved system performance.

Large-scale pose-invariant face recognition using cellular simultaneous recurrent network

In this work, we propose a novel technique for face recognition with +/-90 degrees pose variations in image sequences using a cellular simultaneous recurrent network (CSRN). We formulate the recognition problem with such large-pose variations as an implicit temporal prediction task for CSRN. We exploit a face extraction algorithm based on the scale-space method and facial structural knowledge as a preprocessing step. Further, to reduce computational cost, we obtain eigenfaces for a set of image sequences for each person and use these reduced pattern vectors as the input to CSRN. CSRN learns how to associate each face class/person in the training phase. A modified distance metric between successive frames of test and training output pattern vectors indicate either a match or mismatch between the two corresponding face classes. We extensively evaluate our CSRN-based face recognition technique using the publicly available VidTIMIT Audio-Video face dataset. Our simulation shows that for this dataset with large-scale pose variations, we can obtain an overall 77% face recognition rate.

Detection of resistivity for antibiotics by probabilistic neural networks

This paper presents the use of probabilistic neural networks (PNNs) for detection of resistivity for antibiotics (resistant and sensitive). The PNN is trained on the resistivity or sensitivity to the antibiotics of each record in the Salmonella database. Estimation of the whole parameter space for the PNN was performed by the maximum-likelihood (ML) estimation method. The expectation-maximization (EM) approach can help to achieve the ML estimation via iterative computation. Resistivity and sensitivity of the three antibiotics (ampicillin, chloramphenicol disks and trimethoprim-sulfamethoxazole) were classified with high accuracies by the PNN. The obtained results demonstrated the success of the PNN to help in detection of resistivity for antibiotics.

Homotopic image pseudo-invariants for openset object recognition and image retrieval

This paper presents novel homotopic image pseudo-invariants for face recognition based on pixelwise analysis. An exemplar face and test images are matched, and the most similar image is determined first. The homotopic image pseudo-invariants are calculated next to judge whether the most similar image is the same person as the exemplar. The proposed method can be applied to openset recognition. Recognition task can be performed with or without face databases, while the recognition rate is higher when a database is available. This fact facilitates the recognition of faces and various other objects on the Internet. We benchmark the method using FERET as well as the images downloaded from the Internet.

Individual stable space: an approach to face recognition under uncontrolled conditions

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a "divide and conquer" strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.

A weighted voting model of associative memory

This paper presents an analysis of a random access memory (RAM)-based associative memory which uses a weighted voting scheme for information retrieval. This weighted voting memory can operate in heteroassociative or autoassociative mode, can store both real-valued and binary-valued patterns and, unlike memory models, is equipped with a rejection mechanism. A theoretical analysis of the performance of the weighted voting memory is given for the case of binary and random memory sets. Performance measures are derived as a function of the model parameters: pattern size, window size, and number of patterns in the memory set. It is shown that the weighted voting model has large capacity and error correction. The results show that the weighted voting model can successfully achieve high-detection and -identification rates and, simultaneously, low-false-acceptance rates.

A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs

The face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, we present a method for face recognition based on parallel neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing efficiency decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition result. In particular, the proposed method achieved a 98.75 % recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system.