Human recognition and identi cation are desirable yet challenging for many

applications including surveillance, computer vision, and robotics. Many biomet-

rics have been used in human recognition and identi cation applications. Gait

biometrics is used to recognize and identify people from videos. The gait biomet-

rics has its advantage of capabilities for recognizing people from distance. It is a

very hot topic and has attracted lot of attention in recent years as we deployed

hundreds of thousand surveillance cameras. These surveillance cameras can be

used for analyzing data on the edge [1]. To deploy the deep learning based pro-

cess on the smart devices, process need to be small in size and able to use low

computing ability. This ability can be archived with the optimized learning tech-

nique. Despite having a complex structure and limitations, the discrimination

power of the gait make its a useful and unique component for person recognition

at a distance.

      The work presented in this thesis aims to nd the new strategy for optimized

machine learning and gait recognition and identi cation systems. The object of

the work can be described as:
 

Find the new strategy for gait recognition and identi cation process.

Investigate the possible solution for deploying the machine learning tech-

nique on the low processing power devices.

In order to complete our goal we study di erent strategies to optimize machine

iv A Study on Traditional and Modern Approaches for Person Recognition

learning and human identi cation. This involved traditional bag-of-the-word

framework and deep learning techniques. The goals that we achieved can be

described as:

Optimization of encoding methods used in Bag-of-Word framework: In

this work, our goal is to draw a relationship between descriptors, and use

this relationship to optimize the encoding methods. We used neighboring

descriptors relationship during the encoding the words. We implemented

this new optimization technique on di erent encoding methods and tested

them on image classi cation and gait recognition.

Retrieving data from videos on the base of their gait biometric: In order

to address the challenges of large-scale video databases, we introduce new

gait based retrieval. The gait based retrieval is used to extract the data

of target person from the video database. We used deep hashing method

to solve the retrieval problem, which outperforms the traditional methods

that we implemented. On next stage this retrieved data can be used for

further recognition and identi cation tasks.

Reducing the computational cost by knowledge transfer technique: With

the increasing use of smart devices and IP cameras, video analysis can be

performed on the edge. Deep learning models are complex in size and costly

in computation, it is not possible to use these models directly on smart

devices and for edge based analysis. We use knowledge transfer technique

to reduce processing and memory cost. We implemented this technique

to transfer the functional knowledge of previous trained models to small

Chapter 0. Abstract v

models. Then these small models can be deployed on smart devices and for

the analysis of data at the end terminal.