facedetectionandrecosignationwithpi-200430225418.pptx
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This presentation discusses a face recognition system using a Raspberry Pi. The system uses OpenCV for face detection and eigenfaces for face recognition. It detects faces in images using Haar cascades and compares them to a database of faces to authenticate users. If a match is found, it will open a gate. The hardware includes a Raspberry Pi, camera, and servo motor. The software runs OpenCV and Python code. The system is intended for security applications like controlling access to a gate.
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The document presents a project on face recognition using a Raspberry Pi. It discusses using OpenCV for face detection with Haar cascades and eigenfaces for face recognition with PCA. The system consists of a Raspberry Pi connected to a camera. Faces detected in images are compared to a database of stored faces to recognize individuals and control access to a gate by opening or closing it. The project aims to develop a basic access control system using face recognition for security purposes.
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The document discusses a design team's goal of lowering standby power consumption in appliances via proximity detection. It proposes using passive infrared and infrared or ultrasonic sensors to detect a user's presence and proximity to an appliance in order to smoothly transition the appliance in and out of an energy-efficient standby mode consuming less than 1 watt of power. The document outlines specifications, proposed low-to-mid range and high-end solutions using different sensor combinations, and considerations regarding sensor selection and the future of the product with voice control and status announcements.
bionics_4.ppt
This document provides an overview of the history and types of prosthetic limbs. It discusses the various factors that are considered for different types of amputations and prosthetics for arms and legs. Key points include:
- The types of prosthetics include those for below or above the knee (transtibial and transfemoral) and below or above the elbow (transradial and transhumeral).
- Recent developments have focused on making prosthetics lighter, stronger and more realistic looking using new materials like carbon fiber.
- The C-Leg is an advanced prosthetic knee that can adapt to different activities using onboard microprocessors and sensors.
- Prosthetic hands are more difficult to create than
lecture2-mcu_and_io.pptx
The document defines electrical drives as systems that use electric motors as prime movers for motion control. It discusses the advantages of electrical drives such as flexible control, wide speed and torque ranges, and high efficiency. It describes conventional ward-leonard drive systems and modern drive systems. Modern systems are more compact, efficient, and flexible due to use of power electronic converters and control units. The basic components of electrical drives are described as the power source, motor, power processing unit, control unit, and mechanical load. Different motor types, power sources, and the functions of the power processing and control units are also summarized.
05-Pi-Camera.pdf
The document summarizes the Raspberry Pi camera module. It has an 8 megapixel sensor that can be used to take high-definition video and photos. It connects via ribbon cable to the Raspberry Pi and can be accessed through programming interfaces. The camera module is popular for applications like home security and wildlife cameras. It allows effects like annotations, brightness adjustments, and image filters to be applied.
BHAVESH AGRAWAL.pptx
This document discusses automation and manufacturing topics covered in a class guided by Dr. Md. Taufiq, including electric drives, microcontrollers, and data acquisition systems. It provides an overview of electric drive components like the power modulator and control unit. Microcontrollers are described as integrated devices containing a microprocessor, memory, and I/O ports. The document outlines data acquisition hardware and software used to convert physical sensor measurements into digital values for processing.
222116610_2 Assiment.pptx
The document discusses various topics related to signal conditioning including:
1. Signal conditioning is the process of converting sensor signals into a format suitable for data acquisition and machine control. It involves signal amplification and conversion.
2. A data acquisition system collects signals from sensors, conditions the signals, converts them to digital values, and transfers the data to a computer for processing. It includes sensors, conditioning circuitry, analog-to-digital converters, and data acquisition hardware and software.
3. Microprocessors are now widely used in metrology and measurement systems as they provide accurate, fast measurements and allow automated error correction. Microprocessor-based systems can perform complex measurements and log errors in real-time.
micro manit.pptx
The document discusses data acquisition systems and their basic components and applications. It also discusses microprocessors and microcontrollers, including their history, architecture, and features. Data acquisition systems comprise sensors, measurement devices, and a computer to process acquired data from electrical or physical phenomena. They capture and store data in an easily retrievable format for analysis. Microprocessors are the central processing units of computers and devices, while microcontrollers are self-contained systems with a processor, memory, and I/O in a single package.
05-Pi-Camera.pptx
The document summarizes the features and capabilities of the Raspberry Pi camera module. It describes the camera's 8 megapixel sensor, support for 1080p video recording and still photography. It provides instructions on connecting the camera, taking photos and videos, and using software to manipulate settings like resolution, frame rate, effects, colors and live streaming.
Unit 3.pptx
Robotic end effectors allow robots to perform tasks and can be categorized as grippers or tools. Grippers are used to grasp objects, while tools perform specific tasks like spot welding or spraying. Grippers can be mechanical, vacuum, magnetic, or adhesive-based. Tools attached directly to robots perform operations like spot welding, spraying, or arc welding. The end effector must interface securely with the robot wrist to receive power, control signals, and provide feedback to manipulate objects or tools. Work cells arrange manufacturing resources to improve quality, speed, and cost by optimizing process flow and reducing waste.
Actuators.pptx
Actuators convert energy into mechanical motion or force. They play an important role in robotics and automation by allowing controllers to move robotic joints and limbs. The document discusses various types of actuators including electric actuators like servomotors, stepper motors, and DC motors as well as hydraulic and pneumatic actuators. It provides details on the characteristics, working principles, applications and advantages of common electric actuators like servomotors and stepper motors. Servo motors provide precise rotational or linear movement while stepper motors move in discrete steps making them well-suited for applications requiring positional accuracy.
Lecture-1-Smart-Sensor.ppt
The document discusses intelligent instrumentation and smart sensors. It defines sensors as devices that respond to physical stimuli and convert it into an electrical signal to perform an input function. Actuators are also discussed which perform an output function to control external devices. The general architecture of smart sensors is described including information coding/processing and data compensation. Several types of smart sensors are listed such as temperature, proximity, pressure, gas, smoke, accelerometer, level, image, motion, optical, and gyroscope sensors along with their advantages and disadvantages.
mdml.pptx
The document discusses using machine learning to detect malaria through analyzing images of red blood cells (RBCs). It notes that doctors currently diagnose malaria based on symptoms and medicine, but detection through images could help with consultation. The proposed approach would use image processing on a data set of RBC images to identify parasitized cells and generate an automated report on the parasite count and affected areas to assist diagnosis. The future scope includes developing an automated online doctor using this technique for proper diagnosis and treatment recommendations.
mdml.pptx
Malaria detection can be improved through machine learning by analyzing images of red blood cells. The system would identify parasitized cells in images to help doctors diagnose malaria and determine the appropriate treatment. This could help ensure patients receive the right medicine for their symptoms and condition. With further development, an automated online doctor using this technique may be able to properly diagnose and cure malaria based on analyzed cell images and parasite counts.
rs.pptx
The document summarizes a student project proposal for a smart automated electronic writing pad. Some key points:
- The project aims to develop an electronic notepad that serves as an alternative to physical paper by storing written notes digitally, addressing issues with paper production such as deforestation.
- The student proposes building an operating system for the e-pad that can store written text and data. A sensing material would provide an interface between the touchscreen and a writing pen without detecting human touch.
- Challenges include preventing human touch, costs of importing materials, getting users accustomed to the new technology, and data security concerns related to providing internet access. The estimated cost of building a prototype is 7,
pptr.pptx
A robotic manipulator was designed for walking and assembly tasks in space. The design focused on mobility for locomotion over uneven terrain as well as dexterous manipulation capabilities for assembly work. Key aspects of the design included legged locomotion, force control algorithms, and modular end-effectors to perform various assembly operations.
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- 1. A presentation on: Face recognition with raspberry pi Maulana Azad National Institute of Technology
- 2. BHAVESH AGRAWAL 222116607 KESHAV PURVIYA 222116612 RAHUL DAS ALCOHOLIC 222116610 Guided by: Dr. VIJAY BHASKAR
- 3. Central Idea A system for face detection and recognition based on raspberry pi with open cv programming to control the gate.
- 4. Outlines ❑ Introduction ❑ Block diagram ❑ Algorithm ▪ Face detection ▪ Database ▪ Face recognition ❑ Software ❑ Hardware ❑ The system ❑ conclusion
- 5. Introduction ► A facial recognition system is a computer application for automatically identifying or a verifying a person from a digital image. ► Face detection locate face in whole frame. ► Input image is matched with database images. Authentication is given accordingly. ► Face recognition system is mainly used for security purpose. ► We are using raspberry pi for putting our plan into effect!
- 6. Contd… Recognition is addition of two parts: 1) Face Detection 2) Face recognition
- 7. Block diagram camera [Face detection] (Haar cascade) Person on gate i/ p gate Match?? database [Open cv + python code] SD card memory [Face recognition] (Eigen faces) yes/no RASPBERRY PI motor o/p Yes or no feedback [Door opens/close]
- 8. Face Detection Input from camera Feature extraction Feature vector Face detected? Haar cascade algorithm Detected face highlighted NO YES Face database
- 9. OUTPUT
- 10. Database ► ORL Database 10 different images of each of 40 distinct subjects
- 11. Database ► Own Database 4 different images of each of 4 distinct subjects
- 12. Database create training set and load it Convert face images to face vector Calculate mean average face vector Subtract average face vector from each face vectors to have normalize face vectors Reduce dimensionality of training set Calculate Eigen vectors Represent each image as linear combination of all ‘K’ Eigen vector Select ‘K’ best Eigen faces PCA ALGORITHM
- 13. Eigen faces
- 14. Face recognition(even sem) Input image Convert it into face vector Normalize face vector convert into Eigen space Get weighted vector PCA ALGORITHM Calculate “Distance” b/w input weight vector and all weight vectors of set Decide a threshold level Decide Distance > threshold? Display its name and “matched” Display Unknown yes no
- 16. ►
- 18. Threshol d
- 19. software
- 20. Raspberry Pi B3 GERENIC DIAGRAM HARDWARE PHOTO CPU/GPU USB HUB I/O ETHERNET RAM
- 21. On 1st boot of PI
- 22. The system ❑ Connections: • Interface PI camera with raspberry pi. • Load database, programme and raspbian OS in SD card and inserted in to raspberry pi. • Interface servo motor raspberry pi through GPIO pins. • Give power supply using battery pack or mobile charger.
- 23. The outputs
- 24. conclusion ► By face recognition system we can fulfil the purpose of security. Detection of face is done by image processing. Here we use Open Cv with programming language C++. ► Initially camera will capture the image and face detection algorithm will detect the face in image. Then recognition algorithm is applied on this detected part. In this project we have developed a PCA based face recognition system.
- 25. Reference 1. https://en.wikipedia.org/wiki/Biometrics 2. https://www.cse.unr.edu/~bebis/CS790Q/Lect/Chapters_3_4.ppt 3. http://eyalarubas.com/face-detection-and-recognition.html 4. https://www.raspberrypi.org 5. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/ 6. https://webdocs.cs.ualberta.ca/~nray1/CMPUT466_551/ViolaJones.ppt 7. M. Turk and A. Pentland “Eigen faces for Recognition”, Journal of Cognitive Neuroscience, vol.3, no.1, pp.71-86, 1991, hard copy 8. https://en.wikipedia.org/wiki/OpenCV 9. https://www.python.org/ 10. www.numpy.org/ 11. https://onionesquereality.wordpress.com/2009/02/11/face-recognition-using-eigenfaces 12. https://learn.adafruit.com/raspberry-pi-face-recognition-treasure-box/overview 13. https://www.youtube.com/channel/UCsRvxZErBo0ByyWUX_aVuvg (codacus) 14. http://www.imore.com/how-get-started-using-raspberry-pi 15. http://docs.opencv.org/doc/tutorials/introduction/windows_install/windows_install.html 16. https://www.youtube.com/watch?v=9hb0gYCv3YI 17. www.face-rec.org/databases/