Facial recognition methods were first explored in security systems to identify and compare human faces and is far superior compared to biometric and iris recognition, this technique has been implemented in iris recognition, image detection etc. Recently these methods have been explored in other fields of study and have become a commercial identification and marketing tool. This paper describes the different algorithms of facial recognition and compared their recognition accuracies. The face is detected through Haar Cascades algorithm which is saved into a database, after that, the study intended to compare facial recognition accuracy of the well-known algorithms Eigen faces with PCA, SVM, KNN, and CNN. The results showed out of the three algorithms we used CNN yielded the maximum accuracy.

A Comparative Study on Facial Recognition Algorithms

We have used the Montage image mosaic engine to investigate the cost and performance of processing images on the Amazon EC2 cloud, and to inform the requirements that higher-level products impose on provenance management technologies. We will present a detailed comparison of the performance of Montage on the cloud and on the Abe high performance cluster at the National Center for Supercomputing Applications (NCSA). Because Montage generates many intermediate products, we have used it to understand the science requirements that higher-level products impose on provenance management technologies. We describe experiments with provenance management technologies such as the "Provenance Aware Service Oriented Architecture" (PASOA).

The Application of Cloud Computing to the Creation of Image Mosaics and Management of Their Provenance

This paper focuses on real=time image recognition in video sequences A novel algorithm designed to be used specifically for finger spelling alphabet is introduced along with relevant qualitative comparison to current state of the art. The proposed concept introduces a new image feature selection technique used in comparation process, which significantly improves the recognition of finger spelling alphabets in video sequences. Consideration to the fact that modern technologies are capable of recording videos and capturing images with very high resolution was taken and the proposed novel algorithm is fully capable of efficiently processing also high resolution images.

Algorithm for real-time finger spelling alphabet recognition in video sequences

The face recognition system is a field of research that can help identify a person for either a security system or a search system because faces are distinctive from each person. The development of a human face recognition model requires several main components, such as scale, light, and expression. This study aims to collect accurate samples of Indonesian faces for face detection algorithms. Determining these facial features is challenging to identify Indonesian faces because Indonesia consists of various tribes. In contrast, the classification of tribes in Indonesia consists of Malay and non-Malay tribes. Therefore, this study uses an in-house dataset of Indonesian people's faces which will be tested using the FaceNet algorithm. The results of this study indicate that high accuracy is obtained even when using simple devices. The benefit of this study is that using the in-house method used by researchers can improve the accuracy of test results even though using an in-house dataset.

In-House Facial Data Collection for Face Recognition Accuracy: Dataset Accuracy Analysis

In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the -norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU_PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.

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Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

The study was done in accordance with the deviation from the tradition to modernization of human identification with the purpose of both human convenience and technology maximization. The study aimed to proposed a facial recognition security system, as the main access level for private establishments in order to prevent unauthorized entry of an individual. Facial Recognition delivers convenience, for it is a concrete identification that an individual can have without the need of badges and many other identification cards. It administers efficiency on both the organization and its people. Problems related to stolen, borrowed and lost IDs, are eliminated; thus reducing the cost for password administration or renewal.

Maylane Ballita

Papers

Web and mobile based facial recognition security system using Eigenfaces algorithm