#Manifold learning how to#
#Manifold learning iso#

Although such manifold learning methods can model the manifold structure of the data, they require a large amount of dense data as training samples, which is not applicable to some practical applications. By learning the mapping from environmental space to eigenspace, the structure between adjacent points after projection can be preserved.

#Manifold learning iso#

In order to reflect the nonlinear structure of image features, two classical manifold learning methods have been proposed by previous researchers, which are ISO metric mapping (ISOMAP) and local linear embedding (LLE). Although the computational complexity is reduced, some effective features will be lost while reducing the dimensionality. The face recognition algorithm based on eigenface extracts face features by way of dimensionality reduction.

#Manifold learning how to#

In practical applications, how to solve the influence of environmental factors, human sentiments, and posture changes has become a difficult problem in testing various algorithms. With sufficient samples, single background, and stable ambient light, most algorithms can achieve higher recognition rates. With the increasing innovation of information technology, the processing of images by face recognition technology has become more and more complex. At the same time, face recognition, as a biometric identification technology with high stability, high accuracy, difficult to copy, and easy to be accepted by humans, has a wide range of application prospects in the fields of identity authentication, security monitoring, human-computer interaction, etc. IntroductionДЇace recognition, as a biometric recognition technology, is one of the hot topics in the research fields of pattern recognition, image processing, machine vision, neural networks, and cognitive science in recent years. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks.

At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions.

In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16.