Search Results for "php face recognition"
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25 projects for "php face recognition" with 2 filters applied:
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. ... -
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Hugging Face - Speech To Speech
Open speech-to-speech models and pipelines by Hugging Face toolkit AI
This project from Hugging Face focuses on enabling direct speech-to-speech processing using modern machine learning models. It provides tools and reference implementations that allow audio input to be transformed into audio output without requiring an intermediate text representation. Hugging Face - Speech To Speech builds on recent advances in speech modeling, combining components such as speech recognition, translation, and synthesis into unified pipelines. ... -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
WhisperJAV is an open-source speech transcription pipeline designed specifically for generating subtitles for Japanese adult video content. The project addresses challenges that standard speech recognition models face when transcribing this type of audio, which often includes low signal-to-noise ratios and large numbers of non-verbal vocalizations. Traditional automatic speech recognition systems can misinterpret these sounds as words, leading to inaccurate transcripts. WhisperJAV introduces a specialized pipeline that separates text generation from timestamp alignment, allowing the system to generate transcripts and then align them with audio using forced alignment techniques. ... -
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MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
...These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. MediaPipe is widely used in computer vision and multimedia applications such as hand tracking, face detection, pose estimation, object recognition, and gesture analysis. The framework includes prebuilt solutions that developers can quickly integrate into applications as well as lower-level APIs that allow custom pipeline construction. -
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comfyui-mixlab-nodes
Workflow and speech recognition app
comfyui-mixlab-nodes is a large collection of custom nodes for ComfyUI that turns workflows into interactive apps and adds real-time multimedia, LLM, and TTS capabilities. It introduces a “Workflow-to-APP” concept, where a ComfyUI graph can be transformed into a Web App through an AppInfo node, complete with categories, batch prompts, and editable configurations. The project also brings Real-time Design features like screen capture and floating video nodes, enabling creative pipelines that... -
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Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed... -
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VGGFace2
VGGFace2 Dataset for Face Recognition
VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. -
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Douyin-Bot
Python TikTok bot
...The project uses Python and ADB to connect desktop-side logic with a mobile device. Its original goal was playful and experimental, focused on browsing and identifying content based on computer vision and face analysis. It is best understood as a demonstration of app automation, screen capture, API-based image analysis, and scripted mobile interaction. Its main value is showing how Python can orchestrate a phone app workflow by combining ADB, screenshots, recognition logic, and automated gestures. -
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LibrePhotos
A self-hosted open source photo management service
LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect... -
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lbpcascade_animeface
A Face detector for anime/manga using OpenCV
lbpcascade_animeface is a computer vision project that provides a trained LBP cascade classifier specifically optimized for detecting anime-style faces in images. Built using OpenCV’s cascade classifier framework, the project adapts traditional face detection techniques to stylized anime and manga artwork, where conventional human face detectors often fail. It is commonly used in anime image analysis, automated cropping tools, avatar systems, illustration indexing, and preprocessing pipelines for machine learning datasets. The classifier operates efficiently with relatively low computational requirements, making it practical for real-time or lightweight applications. ... -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
...These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. It includes data loaders, training scripts, neural network architectures, and evaluation metrics that allow researchers to experiment with different embedding learning strategies. ... -
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FaceAccess Facial Recognition System
FaceAccess is an Access Control System based on Facial Recognition
With the growing need to exchange information and share resources, information security has become more important than ever in both the public and private sectors. Although many technologies have been developed to control access to files or resources, to enforce security policies, and to audit network usages, there does not exist a technology that can verify that the user who is using the system is the same person who logged in. FaceAccess provides a prototype implementation as a "login... -
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Voice interactive classroom explores the use of audio technologies for browsing Web-based learning management systems. It includes a set of OKI-compliant voice modules which can be assembled for use upon different LMSs, including Moodle and Sakai.
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Java framework for face detection and face recognition based on different plugin and filter types. Includes Eigenfaces in pure Java, OpenCV detection via JNI, integration of the Betaface.com Web Service, skin color filter, Adobe XMP Export and a nice GUI
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TURing human IDentification, a textual highly accessible alternative to image CAPTCHAs involving the usage of simple phrases and based on the language recognition features of the user, supposedly human.
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Voice Conference Manager uses VoiceXML and CCXML to control speech recognition, text to speech, and voice biometrics for a telephone conference service. Say the names or numbers of people and VCM places them into the call. Can be hosted on public servers
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WHAY is a Video-based Face Recognition tool written in MATLAB. It aims to exploits PCA recognizing as better as possible and tests the limits of this approach.
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wav2vec2-large-xlsr-53-portuguese
Portuguese ASR model fine-tuned on XLSR-53 for 16kHz audio input
wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). -
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wav2vec2-large-xlsr-53-russian
Russian ASR model fine-tuned on Common Voice and CSS10 datasets
...The model supports both PyTorch and JAX and is compatible with the Hugging Face Transformers and HuggingSound libraries. It is ideal for Russian voice transcription tasks in research, accessibility, and interface development. The training was made possible with compute support from OVHcloud, and the training scripts are publicly available for replication. -
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layoutlm-base-uncased
Multimodal Transformer for document image understanding and layout
layoutlm-base-uncased is a multimodal transformer model developed by Microsoft for document image understanding tasks. It incorporates both text and layout (position) features to effectively process structured documents like forms, invoices, and receipts. This base version has 113 million parameters and is pre-trained on 11 million documents from the IIT-CDIP dataset. LayoutLM enables better performance in tasks where the spatial arrangement of text plays a crucial role. The model uses a... -
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Bio_ClinicalBERT
ClinicalBERT model trained on MIMIC notes for clinical NLP tasks
Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. Notes were...
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