• Corpus ID: 212527866

Two Level Decision for Human age prediction using Neural Network

@inproceedings{Dileep2015TwoLD,
  title={Two Level Decision for Human age prediction using Neural Network},
  author={Dileep and Ajit Danti},
  year={2015}
}
A person’s face provides a lot of information such as age, gender and identity. Faces plays an important role in the estimation/prediction of the age of persons, just by looking at their face. In this research, an attempt is made to design a model to classify human age according to features extracted from human facial images using Neural Network (NN). Now a days, Artificial Neural Network (ANN) has been widely used as a tool for solving many decision modeling problems. In this paper a feed… 
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References

SHOWING 1-10 OF 15 REFERENCES
Classification of Age Groups Based on Facial Features
TLDR
An age group classification system for gray-scale facial images is proposed in this paper, using babies, young adults, middle-aged adults, and old adults as age groups in the classification system.
Age Classification from Facial Images
TLDR
The first work involving age classification, and the first work that successfully extracts and uses natural wrinkles, is also a successful demonstration that facial features are sufficient for a classification task, a finding that is important to the debate about what are appropriate representations for facial analysis.
Comparing different classifiers for automatic age estimation
TLDR
The aim of this work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image, which indicates that machines can estimate theAge of a person almost as reliably as humans.
Methodological Approach for Machine based Expression and Gender Classification
TLDR
A neural network-based upright invariant frontal face detection system which can classify the Gender based on the facial information and the use of pi-sigma neural network and the Cyclic Shift Invariance Technique enhances the robustness of classification process.
Spectral Regression based age determination
In this paper, we introduce an advanced age determination technique that combines a feature set derived from an image of the face using multi-factored Principal Components Analysis (PCA) on the shape
Age and gender estimation from facial image processing
TLDR
An image-processing algorithm for wrinkle modeling, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing, and an efficient interface for displaying the relationships among keywords and facial images has been introduced.
Structured Connectivity - Face Model for Recognition of the Human Facial Expressions
TLDR
The facial expressions are recognized by Triangular Features and Rectangular features using comparative analysis demonstrate the effectiveness of the Structured Connectivity face model approach.
High-speed face recognition based on discrete cosine transform and RBF neural networks
TLDR
An efficient method for high-speed face recognition based on the discrete cosine transform, the Fisher's linear discriminant and radial basis function neural networks is presented and achieves excellent performance with high training and recognition speed, high recognition rate and very good illumination robustness.
Human and machine recognition of faces: a survey
TLDR
A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.
Eigenfaces for Recognition
TLDR
A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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