Abstract:
Recognition of visual patterns for real world applications is a complex process that involves
many issues. Varying and complex backgrounds, bad lighting environments, person
independent gesture recognition and the computational costs are some of the issues in this
process. Since human gestures are perceived through vision, it is a subject of visual pattern
recognition. Hand gesture recognition is of higher interest for Human-Computer Interaction
(HCI), due to its widespread applications in virtual reality, sign language recognition, robot
control, medical industry and computer games. The main goal of the research is to propose a
computationally efficient and accurate pattern recognition algorithm for HCI.
Deep learning attempts to model high-level abstractions (features) in data and build strong
feature space for the recognition task. Neural network with five hidden layers was used and
each layer can learn features at a different level of abstraction. However, training neural
networks with multiple hidden layers was difficult in practice. At first, each hidden layer
individually was trained in an unsupervised fashion using autoencoders. After training the
first autoencoder, second autoencoder was trained in a similar way. The main difference is
that features that were generated from the first autoencoder are used as the training data in the
second autoencoder thus decreased the size of the hidden representation, so that the second
autoencoder learns an even smaller representation of the input data. The original vectors in
the training data had 101376 dimensions. After passing them through the first encoder, this
was reduced to 10000 dimensions. After using the second encoder, this was reduced to 1000
dimensions. Likewise at the end, final layer was trained to classify 50 dimensional vectors
into different image classes. The result for the deep neural network is improved by
performing Backpropagation on the whole multilayer network.
Finally, we observed that average test classification error for traditional neural network with
supervised learning algorithm is 3.6% while the error for pre-trained deep neural network is
1.4%. We can conclude that unsupervised pre-training adds robustness to a deep architecture
and it proposes computationally efficient and accurate pattern recognition algorithms for
HCI.
