# Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning
## 3 Learning Model  
### System Overview  
Considering the constraints on power, size, and computational complexity, we explored the monocular vision embedded machine learning framework for the head pose recognition. We modeled the head pose identification as an object classification problem in the domain of computer vision. A machine learning based object classification paradigm is comprised of three steps. Feature identification and extraction is considered as the first and foremost amongst all the three, followed by the feature description which translates the extracted feature to a mathematical form where it can be used as an algebraic operand, and finally a classification model trained on numerous feature descriptors is used for the classification. An alternate approach to the above-mentioned classification scheme is the Artificial Neural Network (ANN) which works analogous to the biological brain. Besides, the main advantage of ANN over the typical classification is the absence of a dedicated feature extraction method for classification. Artificial neural network learning model consists of numerous layers stacked one over the other. Each layer consists of several nodes that encapsulate an \"activation function\". The activation function decides whether the respective neurons should be \"fired or not\". The information for training and testing are parsed into the ANN classification model through the \"input layer\", and the input layer links to the preceding hidden layers, and all layers converge to the fully connected layer and finally to the output layer. Both classification schemes  
Figure 4: Wearable KiliRo robot’s schematic diagram. Figure 3: Wearable KiliRo robot’s head pose orientations. mentioned require an enormous amount of training data to ensure the accuracy of the classification system. Typically, ANN requires comparatively more datasets to yield a better classification result than an SVM. However, if we consider the computational complexity induced by both the ANN and SVM classification models, ANN has an upper hand over the SVM because of the absence of complex computation induced by feature extraction. Furthermore, the ANN algorithms are friendlier for implementing in a computing device that allows parallel execution. From the perspective of real-time implementation of the classification system, computational complexity is among one of the critical challenges to address. In the scenario of a parrot inspired robot, the development of a classification system should acknowledge the following two factors: First, the system should be realizable with minimal requirements for computation. Second, the system should be realizable with minimal usage of sensors for data acquisition. Since we focus on the development of a wearable robot, the structural weight, energy consumption, size, etc. has to be minimized. Hence, the usage of a computation device that requires the development of simple and easily deployable drain surveillance and mosquito detection module. An effective way to address the above-mentioned challenge is the usage of a neural network based on an iterative learning method for classification, since it does not require dedicated feature extraction and description processes. Extensive comparisons of different classification scenarios on SVM and artificial neural networks are reported in the literature. Support vector machines excel in performance over artificial neural networks in terms of time efficiency and accuracy in classification. However, the performance gap between neural networks and SVM in classification problems is almost negligible. Hence, in the case of developing a head pose detection module for a wearable robot, we have done a tradeoff between performance and computational complexity. We modeled the head pose detection to an image classification problem, and we are used convolutional neural networks. Supplemental research work has been reported regarding the development of head pose orientation.