Machine-Readable Faces investigates how facial images have been operationalised, reshaping the conditions under which identity becomes intelligible and computable. Moving across visual semiotics, digital humanities, and computational studies, the book traces the emergence of facial data—from early digitisation experiments to dataset-driven practices—and shows how the face has been reconfigured as a proxy within technical infrastructures.
 
Along this trajectory, facial archives, large-scale image datasets, and training processes within computational infrastructures are analysed at the intersection of visual culture and computational modelling. The book develops a theoretical framework centred on three operative praxes—modelling, archiving, and tagging—through which facial images function as both meta-linguistic operators and infrastructural agents of recognition. This framework offers a critical rearticulation of the epistemological and cultural implications of data-driven identity.
 
Grounded in an interdisciplinary exchange, the book will interest scholars and graduate students working on machine vision, digital culture, and the philosophy of technology, as well as those engaged in visual culture, media sociology, and feminist and gender studies.