README.md

opencv-dnn-face-gender-age-recognition using Deep Learning

Keys	Fields
Author	Subham Das
Project	Facial Recognition
Project Name	Project FRAGD
Technology	OpenCV
Cost	None
Version	v2
Last updated	12 Aug, 2021

In this repo, I am using OpenCV to perform face recognition. To build this face recognition system, I first perform face detection, extract face embeddings from each face using deep learning, train a face recognition model on the embeddings, and then finally recognize faces in both images and video streams with OpenCV.

Models

Download models from

Gender Net : https://www.dropbox.com/s/iyv483wz7ztr9gh/gender_net.caffemodel?dl=0"

Age Net : https://www.dropbox.com/s/xfb20y596869vbb/age_net.caffemodel?dl=0"

Openface : https://storage.cmusatyalab.org/openface-models/nn4.small2.v1.t7

Run Code

Step#0 - Run face_capture.py to store your face image inside dataset. Maximum run for 5 seconds and press q to quit.

python face_capture.py --name <your_first_name>

Step#1 - A deep learning feature extractor to generate a 128-D vector describing a face.

python extract_embeddings.py --dataset dataset --embeddings output/embeddings.pickle --detector face_detection_model --embedding-model openface_nn4.small2.v1.t7

Step#2 - The Linear SVM model will be trained by this script in Step #2. We’ll detect faces, extract embeddings, and fit our SVM model to the embeddings data.

python train_model.py --embeddings output/embeddings.pickle --recognizer output/recognizer.pickle --le output/le.pickle

Step#3 - Face recognize using those trained models alongside gender and age detect/predict

python face_gender_age_recognize.py --detector face_detection_model --embedding-model openface_nn4.small2.v1.t7 --recognizer output/recognizer.pickle --le output/le.pickle

Directory Structure

Example Image

Contact & Support

email: dsubham776@gmail.com

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