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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
ADAPTIVE PART-LEVEL MODEL KNOWLEDGE TRANSFER FOR GENDER
CLASSIFICATION
ABSTRACT
In this letter, we propose an adaptive part-level model knowledge transfer
approach for gender classification of facial images based on Fisher vector (FV).
Specifically, we first decompose the whole face image into several parts and
compute the dense FVs on each face part. An adaptive transfer learning model
is then proposed to reduce the discrepancies between the training data and
the testing data for enhancing classification performance. Compared to the
existing gender classification methods, the proposed approach is more
adaptive to the testing data, which is quite beneficial to the performance
improvement. Extensive experiments on several public domain face data sets
clearly demonstrate the effectiveness of the proposed approach
CONCLUSION
In this letter, we have proposed a novel part-level model knowledge transfer
approach for gender classification of facial images. Unlike the existing methods
in gender classification, our model can reduce the distribution gap between
the training data and the testing data by the model knowledge transfer
learning. Extensive experiments have been conducted to demonstrate the
effectiveness of our new approach.

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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