Unveiling The Veil: Self-Representation in Contemporary Muslim Female Art

Download files
Access & Terms of Use
open access
Copyright: Hammad, Amber
Contemporary Muslim feminist artists, such as Cigdem Aydemir, Sarah Maple and Shirin Neshat, tackle the representation and misrepresentation of Muslim women, within both patriarchal Muslim cultures and the Islamophobic Global North. As this thesis shows, such artists often use the veil to perform Muslim womanhood and their unveiled bodies to claim agency both in and outside of Islamic countries. This practice-led research MFA, developed by Amber Hammad, positions itself in the field of veiling and unveiling Muslim woman’s bodies, building on the work of the aforementioned artists. Drawing on Hammad’s experiences of living in Pakistan and Australia, it analyses the politics of performing Muslim womanhood from a feminist standpoint, utilising strategies of the performance lecture and video art in particular. In the video work The Nude Dupatta — A Performance Lecture (2021) Hammad draws on the work of Hito Steyerl on the politics of images and Andrea Fraser’s work on gendered institutional critique to galvanise her agency as a Muslim female artist. In particular, the work examines the female nude in Islamic art history. In Lower the Gaze: Manuscript Page from خاتون نامه Khatoon Nama #1 (2021) Hammad builds on Shahzia Sikander’s techniques of animation and appropriation and Sara Ahmed’s intersectional feminist theories to connect ideas of visibility and invisibility with the sounds of the Quranic phrase “lower your gaze.” Through these works Hammad expands understandings of Muslim female artists’ engagements with hypervisibility and the politics of veiling.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
Resource Type
Degree Type
Masters Thesis
download Public version.pdf 2.08 MB Adobe Portable Document Format
Related dataset(s)