Deep learning on raspberry Pi3 for face recognition | |
| Author | Nimshi, Kollu |
| Call Number | AIT Thesis no.ISE-19-42 |
| Subject(s) | Deep learning (Machine learning) Human face recognition (Computer science) Neural networks (Computer science) |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Microelectronics and Embedded Systems, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | |
| Abstract | In the present context, there is one big issue regarding intelligent security system using Face Recognition. In fact, it is a valid question-why do we need to implement only Face Recognition as intelligent security system? There is an effort to implement this on low power edge devices like Raspberry Pi3 and improve the accuracy of face recognition software. Even the smallest changes in the light or orientation could reduce the overall performance of recognition leading to more false positives. Though it can be implemented on powerful machines like CPU, GPU etc., yet it is not the best solution as it consumes large size and more power. It also increases the cost and complexity to maintain. Thus, bringing this application into embedded single board computers is very important .Edge computing by reducing deep learning model size is next coming future scope in Embedded System field and see how we can build intelligent system on low power devices. To increase recognition accuracy, the Deep Neural Networks (DNN) can play a vital role for the implementation of deep learning based computer vision tasks. Earlier such systems have been implemented in this area has been done in two factors: (i) end to end learning for the task using a Convolutional neural network (CNN), and (ii) the availability of large scale training datasets. After training the CNN on a desktop PC we employed a Raspberry pi, model B, for the image classification purpose. However, to utilize this CNN model with millions of free parameters on a low power embedded is much more complex and a challenging objective. This constitutes a challenge for embedded vision systems performing edge inference as opposed to cloud processing. Therefore, this led to the idea of using a Intel Neural Compute Stick as a edge inference for accelerating the performance on Raspberry Pi3.The Intel Neural Compute Stick (NCS) provides a possible route for running large - scale neural networks on a low cost, low power, portable unit. Computer vision has made it possible to acquire, process, analyze and extract high-level understanding for digital images and videos. Researchers are also looking at ways to apply the latest advances in facial-recognition technologies to uncontrolled environments, where success rate is maximum up to only 50% only. In this study, Facenet model using one shot learning algorithm is implemented for face recognition and verification on Raspberry Pi3. This system replaces the use of complete trained Facenet model on pi3 by converting this large model into Intel NCS graph and OpenVINO models format by Intel NCS SDK tools and OpenVINO Model Optimizer. With the advanced NCS API and Inference Engine API we are able to perform the inference on pi3 thereby, improving the speed of recognition of objects/ faces. The goal of this experiment is to describe a simple and easy hardware implementation of face recognition system on Raspberry pi3 that run the trained model which is trained on Custom datasets. This system is programmed using Python and is operated and controlled by Raspberry Pi3 with an USB Camera. |
| Year | 2019 |
| Corresponding Series Added Entry | |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Mongkol Ekpanyapong; |
| Examination Committee(s) | Dailey, Matthew N. ;Abeykoon, A.M. Harsha S. ; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship ; |
| Degree | Thesis (M. Eng.) -- Asian Institute of Technology, 2019 |