Abstract
Technology is demanded on to curb crimes, especially image recognition, which can be used to detect suspicious activities. Image, object, and face recognition along with speech identification can be used as great tools to achieve this target. The machine lerning algorithm gave immense capabilities to detect faces, objects, and speech to identify malicious activities, and with several epochs, the accuracy can be enhanced. The chapter applies the various ML algorithms on real-time video data to increase the accuracy and gets satisfactory results in this social cause of utmost importance.
TopIntroduction
Suspicious Activity
The chapter describes initially the basics of initial crime status at world, face recognition and its tells and techniques, machine learning and data analytics and its tool and techniques. For ease of audiences, it also gives a brief review of Data mining, Regressions, AI and CPS. In Next section of the literature review, the famous authors and gist of their work on the content have been enlisted. Then in the application section, the latest classification techniques of support SVM, Dlib, CNN and RNN has been introduced and their application on the data set is reflected. In the result section, the different emotions and object have been recognized and their accuracy has been discussed. Then in the later part the recommendations, novelty, application, limitations have of the research work is explained followed by concluding remarks.
Face Recognition
The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobile health systems, and intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated soon (Onsen Toyger et al., 2003) (Viola, P. et al., 2004).
Machine Learning
An agent is said to learn from experience (E) for some class of tasks(T) performance measure(P), if its Performance at tasks T, as measured by P, improves with experience. E.g. Playing checkers game, Mailing system (Tom Mitchell 1997).
There are different categories of m/c learning
Data Analysis
This is the technique used for extracting useful, relevant, and meaningful information from the huge amount of data in a systematic manner. For the purpose, Parameter estimation (inferring the unknowns), Model development and prediction (forecasting), Feature identification and classification, Hypothesis testing and Fault detection
Python is an object-oriented high-level programming language and widely used with semantic dynamic, used for general-purpose programming. It is interpreted programming language. It is used for: web development (server-side), software development. The way to run a python file is like this on the command line:
helloworld.py
print (“Hello, World!”)
Weka and R
It is a freely available s/w package containing a collection of machine learning algorithms under the GNU (General Public License). It is an open-source. The algorithms present in Weka are all coded in java and they can be used by calling them from their java pod. However with also provides a graphical user interface from which the algorithms can directly be applied to data sets.
R software: R is the programming language. It is freely available s/w. It is used for, statistical and analysis, data manipulation, graphic display. Effective data handling and storage of o/p is possible.
> 2 + 2=4 (Tripathi, R. et al., 2014).
Key Terms in this Chapter
Face Analysis: A facial recognition system is a technology capable of identifying or verifying a person from a digital image or video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from a given image with faces within a database.
Object Detection: It is a computer technology related to Computer Vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection.
Speech Recognition: Speech recognition is an interdisciplinary subfield of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition, or speech to text (STT). It incorporates knowledge and research in the linf=guistics, computer Science and electrical engineering fields.
Machine Learning: Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
SVM: In machine learning, support vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
Suspicious: Having or showing a cautious distrust of someone or something or causing one to have the idea or impression that someone or something is questionable, dishonest, or dangerous or having the belief or impression that someone is involved in the illegal or dishonest activity.