Here are the 10 Free Face Recognition Apps For Android & iOS.
10 Free Face Recognition Apps For Android & iOS
1. BioID Facial Recognition
The BioID app is a multifactor user authenticator – see how well face recognition works today! Developers and companies can easily add biometric authentication to their mobile platforms with a few lines of code. Their end users can log in or authorize transactions securely and conveniently. Forget passwords. Be recognized. EASY MULTIFACTOR AUTHENTICATION WITH MOBILE FACE RECOGNITION WHY BIOMETRICS? Password hacks make headlines almost every week: a password alone is not secure enough. And typing a long, complicated password on a mobile device is difficult and annoying. Multifactor authentication systems using factors like software/hardware tokens or biometric security are becoming more common. Biometric authentication such as facial recognition verifies the physical presence of the user, for easy, strong multifactor authentication using only the user’s mobile device with no extra hardware needed. WHAT DOES THE APP DO FOR ME? The BioID app provides multifactor user authentication (biometrics + mobile device). It is a mobile client for BioID Connect, an identity service based on our BioID Web Service (BWS) – the original ‘biometric as a service’ with our patented ‘fake defender’ liveness detection – and supporting OpenID Connect and OAuth 2.0. • End users can use it as a mobile authenticator to log in to any apps and websites that support the app (including our BWS developer portal). • Developers and companies can easily add secure, convenient face recognition to their mobile platform (websites or native apps), without any knowledge of biometrics. • Anyone can try out our biometric technology and see how well state-of-the-art face recognition works. The app currently supports liveness detection against photo attacks, and challenge-response to prevent video replay attacks. PROTECT YOUR EMPLOYEES As an administrator, improve the security of your authentication simply by supporting BioID Connect through industry standard OpenID Connect / OAuth 2.0 protocols. PROTECT YOUR USERS As a developer, enhance app or website security with just a few lines of code by supporting BioID Connect. We take care of all the biometrics and the associated user interface; you get instant biometric security. PROTECT YOURSELF As a user, get simple and user-friendly stronger security for the growing number of apps and websites that support BioID Connect.
2. Luxand Face Recognition
Just tap any detected face and give it a name. The app will memorize the face and recognize it further. For best results, hold the device at arm’s length. You may slowly rotate the head (or slowly change your location) for the app to memorize you at multiple views. The app can memorize several persons. If a face is not recognized, tap and name it again. The SDK is available for mobile developers: www.luxand.com/facesdk
3. Face Recognition!
4. LogMe Facial Recognition
LogMe is a facial driven search engine that enables to find Facequare is a facial search engine app based on similarity and distance. With LogMe you can: – Upload a picture from your phone camera, gallery or other apps like Facebook and Instagram – The app detects and extracts faces from the photo you uploaded – After a few seconds, you can browse all look alike faces within the LogMe community uploaded faces – You can browse similar faces based on the level of resemblance or the distance of the upload – Eventually it’s possible to send private messages to the members who uploaded the pictures including faces you found
5. Firebase Face Detection
This application is developed to demonstrate Face detection. Application is using Firebase ML kit to detect faces in the image. It is just for detecting faces in image, it is not supporting any face recognition or any kind of security feature.
6. Face Detection and Recognition
Face detection is a computer technology that identifies human faces in digital images. Face detection detects human faces which might then be used for recognizing a particular face. This technology is being used in this application. Face detection is the best face recognition solution. And you can edit photo too. Face detection can Detect and recognize faces. Optimized for social photo application
7. Face Detection-AI
Face Detection is a computer technology that identifies the human faces. Artificial Intelligence and Machine Learning are used in it. Face Detection detects human faces from camera source. With Face Detection u can find the faces and the smile of them accurately. Face Detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face Detection makes each possible face candidate is normalized to reduce both the lightning effect, which is caused by uneven illumination; and the shirring effect, which is due to head movement. Face Detection calculates the fitness value of each candidate is measured based on its projection on the faces with Face Detection. After a number of iterations, all the face candidates with a high fitness value are selected for further verification. Face Detection uses the face symmetry is measured and the existence of the different facial features is verified for each face candidate. Face Detection is used in many applications, often as a part of (or together with) a facial recognition system. Face Detection also used in video surveillance, human computer interface and image database management.
FaceMatchR gives you the ability to perform face detection and face recognition using our Facial Biometrics Engine on the go. You can perform face recognition either from your device’s photo gallery, Facebook or Instagram account. This version only allows you to perform 1:1 face recognition with a 10 face limit for each picture. This will give you a maximum of 100 facial recognition comparisons per pair of images. FEATURES AT A GLANCE – Facial detection of images – Facial recognition of faces in images – Group picture face detection and recognition – Source images from social media accounts – Fast real time image processing – Backend cluster image processing – Low data bandwidth requirements DETAILS FaceMatchR allows you to test our facial recognition system. 1. You select 2 images from one of the three available sources (photo gallery, Facebook, and Instagram). 2. Facial detection is performed on device. 3. Cropped faces are uploaded to our cluster. 4. Facial recognition algorithm performed on cropped faces (1-N). 5. Results are sent back to device. Got any suggestions on how to improve the app? Contact us, we would love to hear it.
9. Face Recognition
Face Recognition can be used as a test framework for several face recognition methods including the Neural Networks with TensorFlow and Caffe. It includes following preprocessing algorithms: – Grayscale – Crop – Eye Alignment – Gamma Correction – Difference of Gaussians – Canny-Filter – Local Binary Pattern – Histogramm Equalization (can only be used if grayscale is used too) – Resize You can choose from the following feature extraction and classification methods: – Eigenfaces with Nearest Neighbour – Image Reshaping with Support Vector Machine – TensorFlow with SVM or KNN – Caffe with SVM or KNN The manual can be found here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/USER%20MANUAL.md At the moment only armeabi-v7a devices and upwards are supported. For best experience in recognition mode rotate the device to left. _______________________________________________________________ TensorFlow: If you want to use the Tensorflow Inception5h model, download it from here: https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip Then copy the file “tensorflow_inception_graph.pb” to “/sdcard/Pictures/facerecognition/data/TensorFlow” Use these default settings for a start: Number of classes: 1001 (not relevant as we don’t use the last layer) Input Size: 224 Image mean: 128 Output size: 1024 Input layer: input Output layer: avgpool0 Model file: tensorflow_inception_graph.pb ——————————————————————————————————— If you want to use the VGG Face Descriptor model, download it from here: https://www.dropbox.com/s/51wi2la5e034wfv/vgg_faces.pb?dl=0 Caution: This model runs only on devices with at least 3 GB or RAM. Then copy the file “vgg_faces.pb” to “/sdcard/Pictures/facerecognition/data/TensorFlow” Use these default settings for a start: Number of classes: 1000 (not relevant as we don’t use the last layer) Input Size: 224 Image mean: 128 Output size: 4096 Input layer: Placeholder Output layer: fc7/fc7 Model file: vgg_faces.pb _______________________________________________________________ Caffe: If you want to use the VGG Face Descriptor model, download it from here: http://www.robots.ox.ac.uk/~vgg/software/vgg_face/src/vgg_face_caffe.tar.gz Caution: This model runs only on devices with at least 3 GB or RAM. Then copy the files “VGG_FACE_deploy.prototxt” and “VGG_FACE.caffemodel” to “/sdcard/Pictures/facerecognition/data/caffe” Use these default settings for a start: Mean values: 104, 117, 123 Output layer: fc7 Model file: VGG_FACE_deploy.prototxt Weights file: VGG_FACE.caffemodel _______________________________________________________________ The license files can be found here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/LICENSE.txt and here https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning/blob/master/NOTICE.txt
10. Betaface Face Recognition
This is early release which support faces comparison with famous people database. In next version we will extend persons database and allow you to create your own private databases for faces search and matching.