In recent years, face recognition technology has become increasingly popular in the mobile application market. Many developers have created free face recognition apps for both Android and iOS platforms, offering users the ability to use their devices’ cameras to identify and recognize faces. These apps use advanced algorithms and machine learning techniques to analyze facial features and match them with existing images in a database. Some of these apps also offer additional features, such as the ability to tag and organize photos, and integrate with social media platforms. While there are many free face recognition apps available, it’s important for users to evaluate their privacy and security features before downloading and using them on their devices.
We have compiled a list of 8 Free Face Recognition Apps For Android & iOS for your consideration, which have undergone rigorous testing and evaluation by our team of experts.
Table of Contents
8 Free Face Recognition Apps For Android & iOS
1. Face Detection and Recognition
The Face Recognition application provides users with the ability to experiment with face recognition technology immediately. Face detection, a computer technology used to identify human faces in digital images, is integrated into the application for recognizing specific individuals. The application is optimized for social photo use and is considered a top solution for face recognition. Additionally, users have the ability to edit photos alongside detecting and recognizing faces.
2. Face Recognition!
The app is designed to detect human faces, compare similar ones, organize people into groups based on visual similarity, and identify previously tagged people in images. Users can upload photos with any number of faces, and the app will locate all faces marked, along with an age estimate for each person and all relevant marks on the face. The face detector is highly accurate, according to the developers.
One of the app’s features is the ability to find look-alike celebrities on the web using face recognition. The quality and resolution of the photo used can affect the accuracy of the results. For the best outcome, users should upload a photo of a frontal face. Results are displayed for each detected face, including age, gender, and celebrity look-alike.
The app can detect one or more human faces in an image and provide facial attributes based on machine learning predictions. The output includes face rectangles to indicate the location of the faces in the image. The app can also show the age, gender, and celebrity look-alike of the uploaded face.
Face verification is another feature of the app that can measure similarity or identity between two faces. Users can check the likelihood that two faces belong to the same person, and the app returns a confidence score indicating the likelihood of the faces belonging to one person. It is recommended to use images containing only a single face for more accurate results.
The app can estimate various facial features such as gender, age, ethnicity, emotion, glasses, mustache, and beard. It can also provide extended measurements like facial and face feature descriptions, the amount of facial hair, and approximate hairstyle.
Finally, the app allows users to easily find similar-looking faces. Given a collection of faces and a new face as a query, the app will return a collection of similar faces.
3. Face Detection-AI
Face Detection is a computer technology that utilizes Artificial Intelligence and Machine Learning to identify human faces in digital images, typically those captured by a camera source. Among its key features, it can accurately detect faces and smiles with high precision.
This technology has gained popularity in various applications, including facial recognition systems, video surveillance, human computer interface, and image database management. Its effectiveness is due to its ability to normalize each possible face candidate, reducing the lighting and shirring effects caused by uneven illumination and head movement, respectively.
To identify the best fit, Face Detection calculates the fitness value of each candidate based on its projection on the faces. After several iterations, candidates with a high fitness value are selected for further verification. The technology also measures face symmetry and verifies the existence of different facial features for each candidate.
Overall, Face Detection is a powerful tool that has numerous practical uses in modern society. Its advanced features make it highly effective in detecting human faces in digital images, making it an indispensable tool for various industries.
4. Firebase Face Detection
This application has been designed to showcase the functionality of face detection. To carry out this task, it utilizes the Firebase ML kit. The primary function of this application is to identify and locate faces within an image. It should be noted that this application does not extend support for any form of facial recognition or security features.
5. LogMe Facial Recognition
LogMe is a facial recognition powered search engine that allows users to find people with similar facial features. Facequare, on the other hand, is a mobile application that uses facial recognition to find people based on similarity and distance.
LogMe users can upload pictures from their phone camera, gallery or other applications such as Facebook and Instagram. The app detects and extracts faces from the uploaded photo. After a few seconds, users can browse through all the faces within the LogMe community that have similar features to the uploaded photo. The search can be refined based on the level of resemblance or distance of the upload.
Once a user finds a match, they can send private messages to the members who uploaded the pictures inclusive of the faces they found.
6. BioID Facial Recognition
The BioID app functions as a multifactor user authenticator, utilizing face recognition technology. Developers and companies can add biometric authentication to their mobile platforms easily with only a few lines of code. End users can then securely and conveniently log in or authorize transactions without the need for passwords.
The use of biometric authentication, such as facial recognition, is becoming increasingly common due to the insecurity of passwords. Multifactor authentication systems require additional factors, such as hardware tokens or biometric security, to ensure enhanced security measures. Biometric authentication, in particular facial recognition, verifies the physical presence of the user, providing easy and strong multifactor authentication with only the user’s mobile device required, and no extra hardware needed.
The BioID app provides multifactor user authentication through biometric and mobile device authentication. It functions as a mobile client for BioID Connect, an identity service based on BioID Web Service (BWS), the original ‘biometric as a service’ with patented ‘fake defender’ liveness detection, and supporting OpenID Connect and OAuth 2.0. The app can be used as a mobile authenticator by end users to log in to any apps and websites that support the app, including the BWS developer portal. Additionally, developers and companies can add face recognition to their mobile platforms without any knowledge of biometrics, and users can try out the biometric technology for themselves. The app currently supports liveness detection against photo attacks and challenge-response to prevent video replay attacks.
Administrators can improve the security of their authentication by supporting BioID Connect using industry-standard OpenID Connect/OAuth 2.0 protocols. Developers can enhance app or website security with just a few lines of code by supporting BioID Connect, with all biometrics and associated user interfaces taken care of by the app. As a user, stronger security is provided for the growing number of apps and websites that support BioID Connect in a simple and user-friendly manner.
7. Luxand Face Recognition
Luxand’s face recognition app allows users to easily identify and remember faces through a simple tap and naming process. The app is able to memorize faces for future recognition, providing optimal results when the device is held at arm’s length. Additionally, users can slowly rotate their head or change their location to allow the app to memorize them at multiple angles. Multiple people can also be memorized by the app. If a face is not recognized, users can simply tap and name it again.
For mobile developers interested in incorporating face recognition technology into their own apps, Luxand provides a software development kit (SDK) available at www.luxand.com/facesdk. This SDK allows developers to access powerful face recognition algorithms and integrate them into their own applications. By providing this SDK, Luxand is providing developers with the tools and resources they need to create cutting-edge face recognition technology within their own apps.
8. Face Recognition
The Face Recognition tool functions as a test framework for various face recognition methods, including Neural Networks with TensorFlow and Caffe. The software includes several preprocessing algorithms such as Grayscale, Crop, Eye Alignment, and Histogram Equalization. Users can also choose from multiple feature extraction and classification methods, such as Eigenfaces with Nearest Neighbour, Image Reshaping with Support Vector Machine, TensorFlow with SVM or KNN, and Caffe with SVM or KNN. The software manual can be found on GitHub, and the tool is currently only available for armeabi-v7a devices and higher. The recognition mode works best when the device is rotated to the left.
For users who wish to use the TensorFlow Inception5h model, they can download it from the link provided in the software. The user will then need to copy the file “tensorflow_inception_graph.pb” to “/sdcard/Pictures/facerecognition/data/TensorFlow” and use the default settings for a start, such as the number of classes, input size, and image mean. If the user prefers to use the VGG Face Descriptor model, they can download it from the link provided and copy the file “vgg_faces.pb” to “/sdcard/Pictures/facerecognition/data/TensorFlow.” The software recommends using caution with this model as it requires devices with at least 3 GB of RAM.
For users who prefer to use the Caffe model, the VGG Face Descriptor model can be downloaded from the provided link, and the files “VGG_FACE_deploy.prototxt” and “VGG_FACE.caffemodel” need to be copied to “/sdcard/Pictures/facerecognition/data/caffe.” The software recommends using caution with this model as it requires devices with at least 3 GB of RAM.
The software also provides links to the license files, which can be found on the GitHub page.