Multimodal Biometric Data Analysis
Budget: $30 – $250 USD
I'm seeking a data scientist or machine learning engineer with significant experience in multimodal biometrics and data analysis. Your primary task will be analyzing a multimodal dataset that includes coded analysis of body language and facial datasets, particularly head movements and facial expressions.
Key Responsibilities:
- Utilise pre-trained CNN models, specifically ResNet50, for feature extraction.
- Implement a hybrid fusion approach by combining two feature fusion methods.
- Classify extracted features using a bi-directional LSTM. Test the trained and validated model on real life samples.
The ideal candidate will have:
- Extensive hands-on experience with multimodal biometrics.
- Proven proficiency in using ResNet50 for feature extraction.
- Strong background in machine learning and data analysis.
- Ability to code and analyze complex datasets.
Skills that will set you apart:
- Experience with hybrid fusion techniques.
- Familiarity with bi-directional LSTM for classification.
- Excellent coding skills for complex dataset analysis.
Key Responsibilities:
- Utilise pre-trained CNN models, specifically ResNet50, for feature extraction.
- Implement a hybrid fusion approach by combining two feature fusion methods.
- Classify extracted features using a bi-directional LSTM. Test the trained and validated model on real life samples.
The ideal candidate will have:
- Extensive hands-on experience with multimodal biometrics.
- Proven proficiency in using ResNet50 for feature extraction.
- Strong background in machine learning and data analysis.
- Ability to code and analyze complex datasets.
Skills that will set you apart:
- Experience with hybrid fusion techniques.
- Familiarity with bi-directional LSTM for classification.
- Excellent coding skills for complex dataset analysis.