• Corpus ID: 57759358

Face Recognition System

  title={Face Recognition System},
  author={Y. Li and Sangwhan Cha},
Deep learning is one of the new and important branches in machine learning. Deep learning refers to a set of algorithms that solve various problems such as images and texts by using various machine learning algorithms in multi-layer neural networks. Deep learning can be classified as a neural network from the general category, but there are many changes in the concrete realization. At the core of deep learning is feature learning, which is designed to obtain hierarchical information through… 
1 Citations



Gradient-based learning applied to document recognition

This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.

Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations that shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.

Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition

A novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided.

Robust Real-Time Face Detection

A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.

OpenFace: A general-purpose face recognition library with mobile applications

It is shown that OpenFace provides near-human accuracy on the LFW benchmark and present a new classification benchmark for mobile scenarios, intended for non-experts interested in using OpenFace and provides a light introduction to the deep neural network techniques the authors use.

Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification

It is empirically shown that high dimensionality is critical to high performance, and a 100K-dim feature, based on a single-type Local Binary Pattern descriptor, can achieve significant improvements over both its low-dimensional version and the state-of-the-art.

Labeled Faces in the Wild: A Survey

A review of the contributions to LFW for which the authors have provided results to the curators and the cross cutting topic of alignment and how it is used in various methods is reviewed.

Eigenfaces for Recognition

A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.

Fast rotation invariant multi-view face detection based on real Adaboost

  • Bo WuH. AiChang HuangS. Lao
  • Computer Science
    Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.
  • 2004
A rotation invariant multi-view face detection method based on Real Adaboost algorithm is proposed and a pose estimation method is introduced and results in a processing speed of four frames per second on 320/spl times/240 sized image.

Robust Real-time Object Detection

A visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described, with the introduction of a new image representation called the “Integral Image” which allows the features used by the detector to be computed very quickly.