• Corpus ID: 3526345

Facial Expression Recognition in Older Adults using Deep Machine Learning

@inproceedings{Caroppo2017FacialER,
  title={Facial Expression Recognition in Older Adults using Deep Machine Learning},
  author={Andrea Caroppo and Alessandro Leone and Pietro Siciliano},
  booktitle={AI*AAL@AI*IA},
  year={2017}
}
Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. [] Key Method Based on the deep learning theory, a neural network for facial expression recognition in older adults is constructed by combining a Stacked Denoising Auto-Encoder method to pre-train the network and a supervised training that provides a fine-tuning adjustment of the network.

Figures and Tables from this paper

Facial Expression Recognition with Active Local Shape Pattern and Learned-Size Block Representations

A new feature descriptor is proposed, namely Local Shape Pattern (LSP), that describes the local shape structure of a pixel’s neighborhood based on the prominent directional information by analyzing the statistics of the neighborhood gradient, which allows it to be robust against subtle local noise and distortion.

Person-Independent Facial Expression Recognition with Histograms of Prominent Edge Directions

In this paper, the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors, is raised.

References

SHOWING 1-10 OF 31 REFERENCES

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Natural facial expression recognition using differential-AAM and manifold learning

Facial Expression Recognition Based on Facial Components Detection and HOG Features

The system detects the face and facial components including eyes, brows and mouths and applies Histogram of Oriented Gradients to encode these facial components as features and a linear SVM is trained to perform the facial expression classification.

Facial Expression Analysis using Active Shape Model

This chapter introduces an automatic recognition system for facial expression from a front view human face image and evaluates facial analysis base on Active shape model (ASM).

Facial expression recognition in ageing adults: from lab to ambient assisted living

An application that uses a webcam and aims to recognise emotions of ageing adults from their facial expression and is devised to be the starting point to enhance the mood of the elderly people living alone at their homes by external stimuli.

Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

FACES—A database of facial expressions in young, middle-aged, and older women and men: Development and validation

Faces is a database comprising N=171 naturalistic faces of young, middle-aged, and older women and men displaying different expressions that is well suited for investigating developmental and other research questions on emotion, motivation, and cognition, as well as their interactions.

Facial Expression Recognition Influenced by Human Aging

It is found that the FER is influenced significantly by human aging, and some schemes to reduce the influence of aging on FER are proposed and evaluated to evaluate the effectiveness in dealing with lifespan FER.

Facial expression recognition based on discriminative scale invariant feature transform

Experiments on the 3D-BUFE database illustrate that the D-SIFT is effective and efficient for facial expression recognition.

A lifespan database of adult facial stimuli

  • M. MinearDenise C. Park
  • Psychology
    Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc
  • 2004
A database of 575 individual faces ranging from ages 18 to 93 is described, developed to be more representative of age groups across the lifespan, with a special emphasis on recruiting older adults.