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Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algor...
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... the noise reduction process must be carried out before the normalization for the best results. Figure 3 shows the °owchart for the proposed PCAaTA algorithm. ...
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... overcome the accuracy problem that exists in current algorithms, a Hybrid PCA and Triangular Algorithm (PCAaTA) is proposed. The PCA has been used as main step in many current approaches for reasons outlined above. All these features make the PCA suitable as a core component in the proposed algorithm. In addition, a normalization process of the human face based on triangle theory is used. The nor- malized images will a®ect some parameters used in the PCA algorithm. Furthermore, the noise reduction process must be carried out before the normalization for the best results. Figure 3 shows the °owchart for the proposed PCAaTA ...
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Facial Recognition System has been widely used in various applications. Nevertheless, their efficiency rate fell dramatically when they were applied under unrestrained environments like the position of face, expression or illumination change. Because of these factors, it is essential to measure and calculate the performance rate of the dissimilar f...
Citations
... Feature extraction is one of the key links of pattern recognition technology [13,14,15]. At present, the common methods of feature extraction in human ear recognition include PCA (Principal Component Analysis) [16,17,18], independent component analysis (ICA) [19,20,21],two-dimensional principal component analysis (2DPCA) [22,23],Fisherface [31,32,33], and Fisher linear discriminant [24,25].However, these methods of single feature extraction can only get a high recognition rate under certain circumstances.Otherwise the recognition effect is not good. Hence, the method of complementary double feature is a way to improve the recognition rate. ...
Ear recognition is a new kind of biometric identification technology now. Feature extraction is a key step in pattern recognition technology, which determines the accuracy of classification results. The method of single feature extraction can achieve high recognition rate under certain conditions, but the use of double feature extraction can overcome the limitation of single feature extraction. In order to improve the accuracy of classification results, this paper proposes a new method, that is, the method of complementary double feature extraction based on PCA and Fisherface, and we apply it to human ear image recognition. Experimental results on the ear image database provided by Beijing University of Science and Technology show that the ear recognition rate of the proposed method is significantly higher than the single feature extraction using PCA, Fisherface or ICA alone.
... For fingerprint recognition, voice recognition, and iris recognition and other features, face recognition is more rapid, vivid, and natural, and the face image can be obtained without interfering with the subject's face [5]. For the face of the recognized person, no other restrictions are required, and the face recognition system does not need a dedicated image collection device, so the cost is also low, and the face recognition technology has gradually been favored by people [6,7]. At present, many researchers in China and other countries have carried out in-depth research on face recognition technology, among which the representative research institutions are the MIT Media Lab and the Artificial Intelligence Laboratory. ...
Due to the fact that problem occurs to the traditional algorithm in the internal change process of the sample set, this paper proposes a distributed virtual reality face recognition algorithm based on the personal intelligent terminal. By the utilization of the face image library with multiple description functions through expansion, the pixel intensity of the face image has data information, the original face image can be used to generate a face image with higher intensity pixels, and the mirror image can increase the detailed data information of the image. By effectively combining source domain images with initialization images, a scalable image set can be generated. According to parameter-free modeling, the image can be extended as a personal intelligent terminal. The test sample images of the same category and different source domains can form an image set and finally use the residual discriminant function to complete the face recognition. Finally, the results of the experimental analysis show that the distributed virtual reality face recognition accuracy analysis method proposed in this paper can not only build a face image database with multiple reconstruction functions but also effectively use the correlation within the face samples to improve the accuracy of face recognition and implement face recognition. Compared with other facial recognition algorithms, this algorithm has higher recognition accuracy and faster recognition speed.
... Other researchers used machine learning detection model. In 2017, [2] used SVM learned model based on Brazilian dataset. The purpose of facial deformation is created for face detection. ...
Facial components extraction is an important step for a large scale of facial processing. However, it is a high challenging task due to the diversity of facial shapes and skin color as well as of the light conditions of the image. This paper presents new model for automatic extracting of facial components. It applies several advanced techniques on several steps. First, a viola face detection is applied to the image to detect face region. Then a percentage of the minimum appearance of the required objects and the number of them are specified as an input to the deformation stage. Those two parameters are used by the adaptive segmentation step. The experiment is applied in 30 public colored image and the final results shows 100% percentage succeeded tests.
... In recent years, principal component analysis (PCA) has been widely used for feature extraction and dimension reduction in pattern recognition applications. 4,8,10,19,29 PCA is a multivariate statistical technique that can explain much of the data variance via orthogonal linear transformation. In our research, PCA is used to¯nd the best subspace such that the signals can be su±ciently represented via this subspace. ...
Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy.
Retrieving fast and accurate information is the key factor in real time biometric systems. The indexed data provides fast access to the pool of closest matching patterns in the database or the input given to the real-time systems. This paper aims to demonstrate the impact of heuristic indexing in realtime face recognition and on face retrieval systems using face skin color and complexion. This paper also presents the comparison of face recognition techniques with and without heuristic indexing and their impact on realtime performance. Proposed method works on both the indexed and non-indexed database. The survey shows that indexing not only improves retrieval speed but also rejects the wrong samples to be inspected, speeding up the overall system. We have proposed a novel heuristic indexing algorithm which locates key pixels on a face, calculates Skin Complexion Value (SCV) and their tolerance range, reduces search space and finally performs classification. The proposed approach yields significant improvement in terms of speed and reduction in False Acceptance Ratio (FAR).
