Hidden Markov Models in Automatic Face Recognition - A Review

Corcoran, Peter; Iancu, Claudia

doi:10.5772/17664

Cited by 2 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of recognition from digital images, there are applications such as face recognition [13], in which a number of different architectures are described, such as using one-dimensional HMMs but allowing transitions between various stacked models. This represents different types of sequences as they are in order from top to bottom and from left to right.…”

Section: _____________________________________________mentioning

confidence: 99%

Comparison of Two Objects Classification Techniques using Hidden Markov Models and Convolutional Neural Networks

Sarmiento

Savage

2020

View full text Add to dashboard Cite

This paper presents a comparison between discrete Hidden Markov Models and Convolutional Neural Networks for the image classification task. By fragmenting an image into sections, it is feasible to obtain vectors that represent visual features locally, but if a spatial sequence is established in a fixed way, it is possible to represent an image as a sequence of vectors. Using clustering techniques, we obtain an alphabet from said vectors and then symbol sequences are constructed to obtain a statistical model that represents a class of images. Hidden Markov Models, combined with quantization methods, can treat noise and distortions in observations for computer vision problems such as the classification of images with lighting and perspective changes.We have tested architectures based on three, six and nine hidden states favoring the detection speed and low memory usage. Also, two types of ensemble models were tested. We evaluated the precision of the proposed methods using a public domain data set, obtaining competitive results with respect to fine-tuned Convolutional Neural Networks, but using significantly less computing resources. This is of interest in the development of mobile robots with computers with limited battery life, but requiring the ability to detect and add new objects to their classification systems.

show abstract

Section: _____________________________________________mentioning

confidence: 99%