This paper proposes new photometric color invariants for object recognition under white illumination and dichromatic reflectance. The authors introduce color models $ c_{1}c_{2}c_{3} $ and $ l_{1}l_{2}l_{3} $ that are invariant to viewing direction, object geometry, and shading. Additionally, $ l_{1}l_{2}l_{3} $ is also invariant to highlights. A new photometric color invariant $ m_{1}m_{2}m_{3} $ is proposed for matte objects, which is invariant to changes in illumination color. The paper evaluates the performance of these color models using a database of 500 images of 3D multicolored objects. The results show that $ l_{1}l_{2}l_{3} $ and hue H followed by $ c_{1}c_{2}c_{3} $ and normalized colors rgb achieve high recognition accuracy under white illumination. $ m_{1}m_{2}m_{3} $ is the only model invariant to changes in illumination color. The paper discusses various color features derived from RGB, including intensity, RGB, normalized colors, hue, and saturation. It presents a reflection model for dichromatic surfaces and shows that normalized colors and saturation are invariant to surface orientation, illumination direction, and intensity. Hue is also invariant for matte surfaces. For shiny surfaces, hue is invariant, but other features are sensitive to highlights. The paper also presents a color ratio feature $ m_{1}m_{2}m_{3} $ that is invariant to illumination color and shading. This feature is derived from the ratio of color values at neighboring image locations. The results show that $ m_{1}m_{2}m_{3} $ provides slightly worse recognition accuracy than $ l_{1}l_{2}l_{3} $ and H. Experiments show that the discriminative power of the histogram matching process is highest for $ l_{1}l_{2}l_{3} $, H, followed by $ c_{1}c_{2}c_{3} $ and rgb. The discriminative power decreases for RGB and I-color features. The results also show that $ m_{1}m_{2}m_{3} $ is insensitive to changes in illumination color, while other features are sensitive. The paper concludes that $ l_{1}l_{2}l_{3} $ followed by H are most appropriate for photometric color invariant object recognition under white illumination. When no constraints are imposed on the imaging conditions, $ m_{1}m_{2}m_{3} $ is most appropriate.This paper proposes new photometric color invariants for object recognition under white illumination and dichromatic reflectance. The authors introduce color models $ c_{1}c_{2}c_{3} $ and $ l_{1}l_{2}l_{3} $ that are invariant to viewing direction, object geometry, and shading. Additionally, $ l_{1}l_{2}l_{3} $ is also invariant to highlights. A new photometric color invariant $ m_{1}m_{2}m_{3} $ is proposed for matte objects, which is invariant to changes in illumination color. The paper evaluates the performance of these color models using a database of 500 images of 3D multicolored objects. The results show that $ l_{1}l_{2}l_{3} $ and hue H followed by $ c_{1}c_{2}c_{3} $ and normalized colors rgb achieve high recognition accuracy under white illumination. $ m_{1}m_{2}m_{3} $ is the only model invariant to changes in illumination color. The paper discusses various color features derived from RGB, including intensity, RGB, normalized colors, hue, and saturation. It presents a reflection model for dichromatic surfaces and shows that normalized colors and saturation are invariant to surface orientation, illumination direction, and intensity. Hue is also invariant for matte surfaces. For shiny surfaces, hue is invariant, but other features are sensitive to highlights. The paper also presents a color ratio feature $ m_{1}m_{2}m_{3} $ that is invariant to illumination color and shading. This feature is derived from the ratio of color values at neighboring image locations. The results show that $ m_{1}m_{2}m_{3} $ provides slightly worse recognition accuracy than $ l_{1}l_{2}l_{3} $ and H. Experiments show that the discriminative power of the histogram matching process is highest for $ l_{1}l_{2}l_{3} $, H, followed by $ c_{1}c_{2}c_{3} $ and rgb. The discriminative power decreases for RGB and I-color features. The results also show that $ m_{1}m_{2}m_{3} $ is insensitive to changes in illumination color, while other features are sensitive. The paper concludes that $ l_{1}l_{2}l_{3} $ followed by H are most appropriate for photometric color invariant object recognition under white illumination. When no constraints are imposed on the imaging conditions, $ m_{1}m_{2}m_{3} $ is most appropriate.