title: Evolutionary Hand Posture Estimation for Image-based Gesture Detection Systems
Creators: name: Saremi, Shahrzad
Publisher: name: Griffith University
Year: 2018
Subjects: subject: Gesture detection systems
subject: Hand posture
subject: Computer hand models
subject: Particle swarm optimisation

description: Hand posture estimation is one of the main phases of hand gesture estimation. It refers to the process of estimating a real hand image obtained from an acquisition device in a computer. The accuracy of the estimated hand model has a direct impact on feature selection and classi cation. The current hand posture estimation techniques are divided into three classes: generative, discriminative, and hybrid methods. In the rst, a model of the hand is generated and improved to model a hand in a computer accurately. In the second class, a database of di erent hand images is created and used to estimate the hand model. Finally, hybrid models use both discriminative and generative methods mostly sequentially. This thesis rst reviews the literature of hand posture estimation using generative methods and identi es the current gaps. The gaps are sensitivity to hand shapes, sensitivity to a good initial posture, di cult hand posture recovery in case of loss in tracking, and lack of addressing multiple objectives to maximise accuracy and minimise computational cost. To ll the gaps identi ed, a new hand model is proposed combining the best features of the current 3D hand models in the literature. Therefore, the rst contribution of this thesis is the proposal of a new 3D hand model with simple shapes and low computational complexity to render. After the proposal of the 3D hand model, it is employed to develop a hand shape optimisation technique as the second contribution. The problem is formulated as a sign-objective problem with several variables and constraints. To nd the global optimum for the single-objective problem formulated, Particle Swarm Optimisation (PSO) is improved and used, as one of the most well-regarded optimisation algorithms in the literature with successful application in both science and industry. This thesis also demonstrates the e ectiveness of the improved PSO in hand posture recovery in case of tracking loss. The last contribution of this thesis is the formulation of the hand posture estimation as a bi-objective problem for the rst time in the literature. The objectives identi ed and used are to minimise the error (maximise accuracy) and minimise the number of points in the point cloud, thus reducing the computational cost. After formulating the problem, Multi-Objective Particle Swarm Optimisation (MOPSO) is employed to estimate the Pareto optimal front as the solution for this bi-objective problem. Both PSO and MOPSO were improved since it was observed that these algorithms are not very e cient for estimating hand postures. Therefore, their performance was improved using an evolutionary operator called Evolutionary Population Dynamics (EPD). The performance of both techniques was tested on test functions and then applied to the problems mentioned in the preceding paragraphs. The case studies in this thesis are 50 hand postures extracted from ve standard datasets in the literature. All the case studies were employed to benchmark the proposed 3D hand model, hand shape optimisation, and hand posture recovery. In the multi-objective section, the same case studies were used. The results show that rstly, the proposed hand model is able to outperform the current hand models due to the better con guration and more uniform point cloud that it o ers. Secondly, the proposed hand shape optimisation can nd an optimal shape for di erent hand sizes and promote hand personalisation. Thirdly, the improved PSO is able to not only nd an optimal shape for the 3D hand model but also to recover from a wrong posture or tracking loss. Finally, this thesis shows that the improved MOPSO can readily estimate the Pareto optimal front for the bi-objective problem. The thesis also considers analysing the high-dimensional results of multi-objective optimisation using parallel coordinates to understand the relationship between the parameters and objectives of this problem for the rst time in the literature.