Face recognition: a convolutional neural-network approach

  title={Face recognition: a convolutional neural-network approach},
  author={Steve Lawrence and C. Lee Giles and Ah Chung Tsoi and Andrew D. Back},
  journal={IEEE transactions on neural networks},
  volume={8 1},
We present a hybrid neural-network for human face recognition which compares favourably with other methods. [] Key Method The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation.

A Pyramidal Neural Network For Visual Pattern Recognition

This paper applies PyraNet to determine gender from a facial image, and compares its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM).

Convolutional Neural Networks with Fused Layers Applied to Face Recognition

The proposed CNN architecture applies fused convolution/subsampling layers that result in a simpler model with fewer network parameters; that is, a smaller number of neurons, trainable parameters, and connections, and it does not require any complex or costly image preprocessing steps.

Face recognition: A novel un-supervised convolutional neural network method

This paper proposes few new twists of unsupervised learning i.e. sparse filtering to seizure effective and distinguishable features of image and shows that the performance of numeral visual identification and detection tasks improves by using these filters in multistage convolutional network architecture i.i. CNN.

Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition

Experimental results reveal that the performance of DCSOM outperforms state-of-the-art methods for noisy digits and achieve a comparable performance with other complex deep learning architecture for other image variations.

Face image analysis with convolutional neural networks

The results presented in this work show that CNNs perform very well on various facial image processing tasks, such as face alignment, facial feature detection and face recognition and clearly demonstrate that the CNN technique is a versatile, efficient and robust approach for facial image analysis.

High-speed face recognition using self-adaptive radial basis function neural networks

The process reduces the computation time at the output layer of the RBFNN by neglecting the ineffective radial basis functions and makes the proposed method to recognize face images in high speed and also in interframe period of video.

Facial Expression Recognition using Image Processing and Neural Network

The developed algorithm for the facial expression recognition system, which uses the two-dimensional discrete cosine transform for image compression and the self organizing map(SOM) neural network for recognition purpose, simulated in MATLAB.

Applying Artificial Neural Networks for Face Recognition

  • T. Le
  • Computer Science
    Adv. Artif. Neural Syst.
  • 2011
A hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently and a new 2D local texture model based on Multi Layer Perceptron for alignment.

Hierarchical Structure Based convolutional Neural Network for Face Recognition

A hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing and causes less training time, fewer numbers of parameters and higher test data accuracy.

Human Face Detection in Visual Scenes

A neural network-based face detection system that uses a bootstrap algorithm for training, which adds false detections into the training set as training progresses, and has better performance in terms of detection and false-positive rates than other state-of-the-art face detection systems.

Distortion Invariant Object Recognition in the Dynamic Link Architecture

An object recognition system based on the dynamic link architecture, an extension to classical artificial neural networks (ANNs), is presented and the implementation on a transputer network achieved recognition of human faces and office objects from gray-level camera images.

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.

Feature-based face recognition using mixture-distance

  • I. CoxJ. GhosnP. Yianilos
  • Computer Science
    Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1996
The results demonstrate that even in the absence of multiple training examples for each class, it is sometimes possible to infer from a statistical model of training data, a significantly improved distance function for use in pattern recognition.

Face recognition using view-based and modular eigenspaces

A modular eigenspace description is used which incorporates salient facial features such as the eyes, nose and mouth, in an eigenfeature layer, which yields slightly higher recognition rates as well as a more robust framework for face recognition.

Human and machine recognition of faces: a survey

A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.

Face Recognition and Gender determination

The system presented here is a specialized version of a general object recognition system that can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard.

View-based and modular eigenspaces for face recognition

A modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer, which yields higher recognition rates as well as a more robust framework for face recognition.

Learning Human Face Detection in Cluttered Scenes

This paper presents an example-based learning approach for locating vertical frontal views of human faces in complex scenes by means of a few view-based “face” and “non- face” prototype clusters, and shows empirically that the prototypes chosen are critical for the success of the system.

The self-organizing map