Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering transform/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.
The authors in REF proposed an algorithm which uses a convolutional network named scattering transform/network which delivers a multi-layer representation of the signal and is invariant to translation, small deformation, and rotation.
Face recognition using scattering convolutional network
2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
Computer Science