Update README.md

README.md

@@ -23,12 +23,13 @@ Integrating 3D mannequin images into training scenarios boosts the effectiveness
 
 ## Dataset Description:
 
-Scope: Features over 100 mannequins from retail environments.
-Diversity: Includes female, male, and children mannequins, some sporting natural hair.
-Image Capture: Utilizes both selfie and frontal camera perspectives.
-Variations: Encompasses accessories such as glasses, sunglasses, scarves, and hats.
-Lighting Conditions: Offers a range of lighting scenarios for well-rounded algorithm training.
-Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰
+- Scope: Features over 100 mannequins from retail environments.
+- Diversity: Includes female, male, and children mannequins, some sporting natural hair.
+- Image Capture: Utilizes both selfie and frontal camera perspectives.
+- Variations: Encompasses accessories such as glasses, sunglasses, scarves, and hats.
+- Lighting Conditions: Offers a range of lighting scenarios for well-rounded algorithm training.
+
+
 Best Used For Anti-Spoofing Training: The dataset’s 3D characteristics elevate the training efficiency of anti-spoofing algorithms, ensuring a more robust learning experience for detection models.
 
 Keywords: 3D Mannequin Face Dataset, Liveness Detection Models, Anti-Spoofing, Comprehensive Dataset, Realistic Features, Detection of Genuine Faces, Sophisticated Spoofing Scenarios, Diverse Lighting, Retail Mannequins, Variability in Facial Accessories, Frontal Camera Usage, Enhanced Anti-Spoofing Algorithms, Security in Biometric Systems, Comprehensive Exposure, Facial Recognition Training, Mannequin-Based Anti-Spoofing, 3D Mask Simulation.