ESP32-CAM Face Recognition Door Lock System (2023) (2023)

FAQs

How do I use ESP32-CAM face recognition? ›

To start the video streaming, click on the 'Start Stream' button at the bottom of the page. To recognize the faces with ESP32-CAM, first, we have to enroll the faces. For that, turn on the Face recognition and detection features from settings and then click on the Enroll Face button.

Viola-Jones algorithm is an object detection technique focusing on the faces in an image or video. It is operative only on frontal faces. The tracking of the face is done with the help of an ESP32-CAM microcontroller.

In the face recognition approach, a given face is compared with the faces stored in the database in order to identify the person. The aim is to search out a face in the database, which has the most similarity with the given face.

Benefits of Face Recognition Access Control Systems

Face recognition verifies who you are, not what you carry (key card) or what you know (PIN). Hence, it's inherently more secure for controlling access to buildings. Hands free – You can simply walk up and look at the reader and the door unlocks!

To connect to the access point on your computer, go to the Network and Internet Settings, select the “ESP32-Access-Point“ and insert the password. And it's done! Now, to access the ESP32-CAM web server page, you just need to type the IP address 192.168. 4.1 in your browser.

The most common type of machine learning algorithm used for facial recognition is a deep learning Convolutional Neural Network (CNN). CNNs are a type of artificial neural network that are well-suited for image classification tasks.

The most popular and well-known machine learning algorithm for face recognition is the Viola-Jones algorithm. It detects photos in several stages: feature definition, feature assessment, feature classifier definition, and classifier cascade check.

C++ is considered to be the fastest programming language, which is highly important for faster execution of heavy AI algorithms. A popular machine learning library TensorFlow is written in low-level C/C++ and is used for real-time image recognition systems.

While face detection trains a computer to pick out a human face, face recognition software will analyze the image. It will turn the image into a set of data about your facial features. This can include the distance between your eyes, forehead, and chin, and other geometric measurements.

What is the cost of face recognition door lock system? ›

Face Recognition Attendance Recording System With Access Control Door Lock at Rs 14797 | Face Recognition Time Attendance System in Faridabad | ID: 24005141712.

Cons Of Having A Smart Lock

Smart locks communicate with your phone, usually via Bluetooth or Wi-Fi. If your phone is stolen, you lose it or it dies, you could get locked out if you don't have a backup plan (like logging in to your account on another device).

Express Mode: Unlock the door just by holding your iPhone or Apple Watch near the lock. Require Face ID or Passcode: Hold your device near the lock, then use Face ID or Touch ID to unlock it.

The ESP32-CAM is in deep sleep mode with external wake up enabled. When motion is detected, the PIR motion sensor sends a signal to wake up the ESP32. The ESP32-CAM takes a photo and saves it on the microSD card. It goes back to deep sleep mode until a new signal from the PIR motion sensor is received.

If the router is connected to the Internet, then the ESP32 can access the Internet. The ESP32 can behave as a client: make requests to other devices connected to the network, or as a server: other devices connected to the network will send requests to the ESP32. In both cases, the ESP32 can access the Internet.

The main objective of facial recognition is to identify individuals, whether individually or collectively. The number of false positives can vary, depending on the technology used for facial recognition. The best face identification algorithm has an error rate of 0.08%.

Input image guidelines

For face recognition, you should use an image with dimensions of at least 480x360 pixels. For ML Kit to accurately detect faces, input images must contain faces that are represented by sufficient pixel data. In general, each face you want to detect in an image should be at least 100x100 pixels.

There are different types of algorithms which can be used for Face Recognition that are PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), ICA (Independent Component Analysis), EBGM (Elastic Bunch Graph Matching), Fisherfaces.

What is the accuracy of Face_recognition? ›

The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark. This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!

In ideal conditions, facial recognition systems can have near-perfect accuracy. Verification algorithms used to match subjects to clear reference images (like a passport photo or mugshot) can achieve accuracy scores as high as 99.97% on standard assessments like NIST's Facial Recognition Vendor Test (FRVT).

Speed: FaceStation 2 can match upto 3000 matches per second (ultra-Fast performance), which makes this device as the world's fastest face recognition technology in its class.

With Python, some data, and a few helper packages, you can create your very own. In this project, you'll use face detection and face recognition to identify faces in a given image. In this tutorial, you'll build your own face recognition tool using: Face detection to find faces in an image.

Faces are made of thousands of fine lines and features that must be matched. The face recognition using Python, break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to face Recognition Python is the latest trend in Machine Learning techniques.

OpenCV is a popular computer vision library available in Python. It was originally written in C/C++ and now provides Python bindings. It has machine learning-backed methods for advanced face detection. The algorithms detect faces in an image by breaking it down into thousands of patterns and features that it matches.

The top six ethical concerns related to facial recognition systems include racial bias and misinformation, racial discrimination in law enforcement, privacy, lack of informed consent and transparency, mass surveillance, data breaches, and inefficient legal support.

Facial recognition technology has allowed police departments across the U.S. to compare the faces of criminal suspects against other existing photos, but the tech has also proven controversial.

Causes of prosopagnosia (face blindness)

It can happen: if you do not develop the ability to recognise faces – this is the most common type and may run in families. from brain damage, such as following a stroke, head injury, inflammation of the brain (encephalitis), or Alzheimer's disease.

Passwords and biometrics are used to verify your identity. Biometrics are strong to begin with, and passwords may not be. But passwords will work even if you have lotion on your hands or acquire a new scar. Biometrics allow you to get into a device or app more quickly than passwords.

Does Face ID use camera or sensor? ›

Face ID uses the TrueDepth camera and machine learning for a secure authentication solution. Face ID data – including mathematical representations of your face – is encrypted and protected with a key available only to the Secure Enclave.

Fingerprint recognition can confirm individual identity more accurately than facial recognition systems. However, this may change as facial recognition systems become increasingly integrated with iris recognition, another biometric authentication method with high accuracy.

Homeowners can expect to pay around $330 to have a smart door lock installed, although the price ranges from $200 to $460 on average. The national average cost to install a smart door lock is $330, with most homeowners paying somewhere between $200 and $460.

Best video doorbell with facial recognition

Google's Nest Doorbell with battery launched in 2021 with a solid set of features and a reasonable $180 price tag. Without any monthly fee, you get person, vehicle and package alerts, and a few hours of rolling storage.

The locks, the installation labor costs, and even the type of car can all change the price. But, in general, you can expect to pay anywhere from $300-$600 for labor alone. And while you can find cheap and generally ineffective automatic car door locks for as little as $100.

Demonstration. Open your browser and type the ESP32-CAM IP Address. Then, click the “CAPTURE PHOTO” to take a new photo and wait a few seconds for the photo to be saved in SPIFFS. Then, if you press the “REFRESH PAGE” button, the page will update with the latest saved photo.

The ESP32-CAM is in deep sleep mode with external wake up enabled. When motion is detected, the PIR motion sensor sends a signal to wake up the ESP32. The ESP32-CAM takes a photo and saves it on the microSD card. It goes back to deep sleep mode until a new signal from the PIR motion sensor is received.

What do I need to use a Windows Hello webcam? The webcam, a USB cable to connect it to your PC, and a desktop or laptop with Windows 10 or Windows 11 are all you need.

Selecting the Board and Port

Now connect the ESP32-CAM to your computer using a USB cable. Then, navigate to Tools > Port and choose the COM port to which the ESP32-CAM is connected. That's it; the Arduino IDE is now set up for the ESP32-CAM!

The ESP32 chip contains 520KB of RAM. While it's sufficient for most projects, others may need more memory. To increase the capacity of the microcontroller, the manufacturer can add a memory chip to the board. This external RAM chip is connected to the ESP32 via the SPI bus.

Can ESP32 CAM record video? ›

Story. The SpyCam project uses the “AI Thinker ESP32-CAM” module which has a 2MP Camera, 10 I/O pins, an on-chip LED and one MicroSD slot to store the images and videos. With its tiny sized camera and 240MHz CPU is able to provide images and videos in a fair quality.

Overview: ESP32 CAM Based Object Detection & Identification

This tutorial introduces the topic of ESP32 CAM Based Object Detection & Identification with OpenCV. OpenCV is an open-sourced image processing library that is very widely used not just in industry but also in the field of research and development.

The ESP32 Cam captures the images and then sends them to Google drive. The images can be captured in two different ways. We can make it fully automatic by using a delay. So, the ESP32 Cam will capture the image after a fixed delay and then send it to Google drive.

One of the quick tricks you can try is to quickly turn the motion sensor ON, OFF, ON to override the motion detection and make the light stay on. To go back to motion detection mode, turn the motion sensor off, wait for about 10 seconds, and then turn it on again.

There are 3 settings on a motion sensor light: sensitivity, timer, and lux. These 3 settings determine how sensitive the light is to movement, how long the light will stay on after detecting movement, and what level of darkness the light will turn on in.

DESCRIPTION. The ESP32-CAM is a small size, low power consumption camera module based on ESP32. It comes with an OV2640 camera and provides onboard TF card slot. The ESP32-CAM can be widely used in intelligent IoT applications such as wireless video monitoring, WiFi image upload, QR identification, and so on.

Windows Hello is a more personal, more secure way to get instant access to your Windows 11 devices using a PIN, facial recognition, or fingerprint. You'll need to set up a PIN as part of setting up fingerprint or facial recognition sign-in, but you can also sign in with just your PIN.

Using your Windows 10 or Windows 11 laptop's built-in webcam, Hello uses facial recognition to get you in and working in under two seconds. Unfortunately, not every webcam works with Windows Hello.