Search Results for "face recognition using java" - Page 4
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Showing 90 open source projects for "face recognition using java"
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Dragon Character Training is a PalmOS program using stroke recognition to help you learn to read and write Chinese characters. It is good for learning Mandarin vocabulary and characters.
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The OpenCV DLLCapture project goal is to provide an easy way for using the Intel OpenCV library face recognition feature within custom built applications.
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Mouse Gestures is an open source (LGPL) pure Java library for recognition and processing mouse gestures. With Mouse Gestures you have a fast way to execute commands without using the keyboard, menus or toolbars. Compatible with JDK1.3, JDK1.4 and JDK1.5.
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The Human Body Project allows computers to better understand and interact with people. Using a webcam the system can detect and recognise individuals (face recognition), identify their gaze direction, facial expressions and upper body postures.
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xmillum is a framework for visualizing document recognition results and for creating interactive document recognition applications. It can be freely extended using Java plugins.
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Face-detection-VB
face recognition software using OpenCv library and development face id
face recognition software using OpenCv library and development face identity pattern with detection object with full detail software description watch video : https://youtu.be/-U74iHcJJ48 -
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eky-facerecognition-livenessdetection
Face recognition, Liveness detection, ID document recognition
This is the android project for ekyc by using face recognition, liveness detection and ID document recognition technologies. It works on premise meaning that user data never leaves the device(mobile phone, server, etc) Face recognition is super face and unbiased. It's using 3D passive liveness detection to detect printed photos, video replay, 3D masks and deep fakes. -
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voice-activity-detection
Detects speech activity in audio using pyannote.audio 2.1 pipeline
The voice-activity-detection model by pyannote is a neural pipeline for detecting when speech occurs in audio recordings. Built on pyannote.audio 2.1, it identifies segments of active speech within any audio file, making it valuable for preprocessing tasks like transcription, diarization, or voice-controlled systems. The model was trained using datasets such as AMI, DIHARD, and VoxConverse, and it requires users to authenticate via Hugging Face for access. To use the model, users must accept... -
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bert-base-chinese
BERT-based Chinese language model for fill-mask and NLP tasks
bert-base-chinese is a pre-trained language model developed by Google and hosted by Hugging Face, based on the original BERT architecture but tailored for Chinese. It supports fill-mask tasks and is pretrained on Chinese text using word piece tokenization and random masking strategies, following the standard BERT training procedure. With 12 hidden layers and a vocabulary size of 21,128 tokens, it has approximately 103 million parameters. The model is effective for a range of downstream NLP... -
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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). It achieves a WER of 11.3% (or 9.01% with LM) on Common... -
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wav2vec2-large-xlsr-53-russian
Russian ASR model fine-tuned on Common Voice and CSS10 datasets
wav2vec2-large-xlsr-53-russian is a fine-tuned automatic speech recognition (ASR) model based on Facebook’s wav2vec2-large-xlsr-53 and optimized for Russian. It was trained using Mozilla’s Common Voice 6.1 and CSS10 datasets to recognize Russian speech with high accuracy. The model operates best with audio sampled at 16kHz and can transcribe Russian speech directly without a language model. It achieves a Word Error Rate (WER) of 13.3% and Character Error Rate (CER) of 2.88% on the Common Voice... -
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clip-vit-base-patch32
Zero-shot image-text matching with ViT-B/32 Transformer encoder
clip-vit-base-patch32 is a variant of OpenAI's CLIP (Contrastive Language–Image Pretraining) model using a Vision Transformer base (ViT-B/32) for image encoding. It matches images and text by learning a shared embedding space and computing cosine similarity between (image, text) pairs. This model enables zero-shot classification by comparing an image to multiple text prompts without requiring task-specific training. Trained on a large, diverse corpus of internet image-text pairs, it supports... -
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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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clip-vit-large-patch14-336
CLIP model for zero-shot image-text tasks using 336x336 patches
... apply this model to perform tasks like zero-shot image recognition, image search with text, or text generation from visual cues—without task-specific training. -
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MMUAVC
A Multimodal approach to control a Unmanned Aerial Vehicle
Welcome to Multimodal Unmanned Aerial Vehicle Controller (MMAR) This project intends to develop a multimodal interface which gives a human operator capability of controlling a QuadRotor UAV efficiently and more easily. During this project we use a simulated QuadRotor called AirRobot in a simulation environment for development of robots, USARSim. There are some aspects involved in this project including touch, pose tracking, gesture recognition, voice recognition, etc and we try to utilize...
