Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

https://www.mdpi.com/1424-8220/20/2/342/pdf

Face Recognition Systems: A Survey

Reviewing ensemble classification methods in breast cancer.

Face recognition is the process of identifying people through facial images. It has become vital for security and surveillance applications and required everywhere including institutions, organizations, offices, and social places. There are a number of challenges faced in face recognition which includes face pose, age, gender, illumination, and other variable condition. Another challenge is that the database size for these applications is usually small. So, training and recognition become difficult. Face recognition methods can be divided into two major categories, appearance-based method and feature-based method. In this paper, the authors have presented the feature-based method for 2D face images. speeded up robust features (SURF) and scale-invariant feature transform (SIFT) are used for feature extraction. Five public datasets, namely Yale2B, Face 94, M2VTS, ORL, and FERET, are used for experimental work. Various combinations of SIFT and SURF features with two classification techniques, namely decision tree and random forest, have experimented in this work. A maximum recognition accuracy of 99.7% has been reported by the authors with a combination of SIFT (64-components) and SURF (32-components).

2D-human face recognition using SIFT and SURF descriptors of face’s feature regions

Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images.

An evaluation of image-based structural health monitoring using integrated unmanned aerial vehicle platform

The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved
to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by
Herbert Bay, attracts people's attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with
comparable or even better performance with SIFT. Because each of SURF feature has only 64 dimensions in general and
an indexing scheme is built by using the sign of the Laplacian, SURF is much faster than the 128-dimensional SIFT at
the matching step. Thus based on the above advantages of SURF, we propose to exploit SURF features in face
recognition in this paper.

Face recognition using SURF features

Eye location is an important step in face recognition. Locate eyes under various illumination conditions is also an important step especially for most of illumination normalization algorithms. In this paper, an algorithm uses a group of EigenFeature templates and Gabor feature templates to locate eye under various illumination conditions is proposed. Experiment results show that the performance of this algorithm is good and robust under widely changed illumination conditions.

Anni Cai

Papers

Face recognition using SURF features

Eye Location under Various Illumination Conditions