Iris recognition systems are widely deployed in various access control applications such as highly secured border control. Recent studies have demonstrated the vulnerability of iris sensors to various types of Presentation Attack Instruments (PAI) that include, including print attacks, prosthetic eyes, and textured contact lenses. Among these PAI, textured/patterned or color cosmetic contact lenses can be used to conceal the identity and thus can be a potential threat to iris recognition systems. In this study, we propose a novel approach to detect textured contact lens attacks by leveraging complementary information from a Multi-Spectral (MS) iris capture device. The method combines deep features extracted from AlexNet and ResNet50 through a fusion framework. The proposed approach utilizes a specialized MS iris capture device capable of capturing iris images across five distinct spectral wavelengths: 800 nm, 830 nm, 850 nm, 870 nm, and 980 nm. We then employed the proposed algorithm to extract the deep features using AlexNet and ResNet50 across five different spectral bands that are combined at the comparison score level to accurately detect textured contact lens attacks on iris recognition systems. To achieve this, we present a new dataset focused on presentation attacks, collected using a MS iris capture device and featuring two types of textured contact lenses. The new dataset comprises 2800 artefact samples corresponding to two different types of contact lenses and 1200 bona fide samples captured in five different spectral bands. Experimental evaluation demonstrates the outstanding performance of texture-based features on multi-spectral iris samples in detecting both known and unknown contact lens iris attacks.