Aggregation Of Lark Vectors For Facial Image Classification

MATHEMATICAL MODELLING AND SCIENTIFIC COMPUTING WITH APPLICATIONS, ICMMSC 2018(2020)

引用 1|浏览1
暂无评分
摘要
Face recognition is prevailing to be a key aspect wherever there is a need for interaction between humans and machines. This can be achieved by containing a set of sketches for all the possible individuals and then cross-validating at necessary circumstances. We propose a mechanism to fulfil this task which is centred on locally adaptive regression kernels. A comparative study has been presented at encoding stages as well as at the classification stages of the pipeline. The results are cautiously examined and analyzed to deduce the best mechanism out of the proposed methodologies. All the ideologies have been tested for multiple iterations on benchmark datasets like ORL, grimace and faces 95. The vectorized descriptors have been subjected to encoding using slightly refined methods of feature aggregation and clustering to assist classifiers in imputing the test subjects to their respective classes. The encoded vectors are classified using Gaussian Naive Bayes, Stochastic Gradient Descent classifier, linear discriminant analysis and K Nearest Neighbour to accomplish face recognition. An inference on sparse nature of locally adaptive regression kernels was made from the experimentation. A rigorous study regarding the discrepancies of the performance of LARK descriptors is reported.
更多
查看译文
关键词
Adaptive Kernels, Bayesian, Classifier, Feature aggregation, Sparse features, Image, Classification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要