Soft biometric techniques can perform a fast and unobtrusive identification within a limited number of users, be used as a preliminary screening filter, or combined in order to increase the recognition accuracy of biometric systems.
Age estimation based on face images and pre-trained Convolutional Neural Networks
Link to source code (Matlab):
Link to source code of demonstration using a webcam (Matlab):
Age estimation based on face images plays an important role in a wide range of scenarios, including security and defense applications, border control, human-machine interaction in ambient intelligence applications, and recognition based on soft biometric information.
Recent methods based on deep learning
have shown promising performance in this field. Most of these
methods use deep networks speciﬁcally designed and trained to
cope with this problem. There are also some studies that focus on
applying deep networks pre-trained for face recognition, which
perform a ﬁne-tuning to achieve accurate results.
in this paper, we propose a preliminary study on increasing
the performance of pre-trained deep networks by applying post-
processing strategies. The main advantage with respect to fine-
tuning strategies consists of the simplicity and low computational
cost of the post-processing step. To the best of our knowledge,
this paper is the first study on age estimation that proposes the
use of post-processing strategies for features extracted using pre-
trained deep networks. Our method exploits a set of pre-trained
Convolutional Neural Networks (CNNs) to extract features from
the input face image. The method then performs a feature level
fusion, reduces the dimensionality of the feature space, and
estimates the age of the individual by using a Feed-Forward
Neural Network (FFNN).
Method for the weight estimation of walking individuals
The weight is a soft biometric trait which offers a good compromise between distinctiveness and permanence, and is frequently used in forensic applications. However, traditional weight measurement techniques are time-consuming and have a low user acceptability.
We propose a method for a contactless, low-cost, unobtrusive, and unconstrained weight estimation from frame sequences representing a walking person. The method uses image processing techniques to extract a set of features from a pair of frame sequences captured by two cameras. Then, the features are processed using a computational intelligence approach, in order to learn the relations between the extracted characteristics and the weight of the person.
We tested the proposed method using frame sequences describing eight different walking directions, and captured in uncontrolled light conditions. The obtained results show that the proposed method is feasible and can achieve a view-independent weight estimation, also without the need of computing a complex model of the body parts.
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